[0-9.]+)\s+[-][-][-]\s+[-][-][-]\s*$', stuff, re.M | re.S)
+ if m is None:
+ print 'Could not find ~merged~ line in file','output-galax.txt'
+ print '----- stuff begins -----'
+ print stuff
+ print '----- stuff ends -----'
+ sys.exit()
+ Cmerged = float(m.group('coverage'))
+ Imerged = float(m.group('Ipct'))
+ D = float(m.group('D'))
+ print ' %12.5f %12.5f %12.5f' % (Cmerged, Imerged, D)
+ outf.write('%12d %20s %12.5f %12.5f %12.5f\n' % (run, 'concat_topo %d' % (i+1,), Cmerged, Imerged, D))
+ else:
+ print ' %12.5f %12.5f %12.5f' % (Cmerged, Imerged, D)
+ outf.write('%12d %20s %12.5f %12.5f %12.5f\n' % (run, 'concat_topo_brlen', Cmerged, Imerged, D))
+
+def getRunSet(names):
+ numbers = set()
+ n = 0
+ for name in names:
+ m = re.match('run(\d+)[a|b]', name)
+ assert m is not None
+ num = int(m.group(1))
+ numbers.add(num)
+ n += 1
+ assert n == 2*len(numbers)
+ return numbers
+
+def getNumGenes(names):
+ assert len(names) > 0
+ gist_output_filename = os.path.join(names[0],'gist-output.txt')
+ m = re.search('number_of_genes\s*=\s*(\d+)', open(gist_output_filename).read(), re.M | re.S)
+ assert m is not None
+ return int(m.group(1))
+
+if __name__ == '__main__':
+ outf = open('paired_runs.txt', 'w')
+ outf.write('%12s %20s %12s %12s %12s\n' % ('run', 'dataset', 'coverage', 'info', 'D'))
+
+ names = sorted(glob.glob('run*'))
+ runset = getRunSet(names)
+ ngenes = getNumGenes(names)
+ for run in runset:
+ doGalax(run, ngenes, outf)
+
+ outf.close()
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/gist.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/gist.py
new file mode 100644
index 0000000..ce577c5
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/gist.py
@@ -0,0 +1,689 @@
+# coding: utf-8
+import random, os, sys, subprocess, re, shutil, glob, tempfile, stat, math
+import tree, yule, coal, fixed, plot
+master_seed = 1895995
+top_level_dir = 'g' # this directory will be created to hold output
+
+# Expects location of the seqgen executable to be specify in environmental variable SEQGEN
+try:
+ seqgen_location = os.environ['SEQGEN']
+except KeyError:
+ sys.exit('Set SEQGEN environmental variable specifying path to seq-gen executable')
+print "seqgen_location =",seqgen_location
+
+# How to set starting_rep and num_reps. Suppose you want to do 100 reps total,
+# but doing so would cause you to exceed the disk quota on your cluster. Thus, you want to
+# do the work in 2 batches (1-50 and 51-100) but would like to be able to repeat the
+# results exactly had you done everything at once (1-100). To do this, set
+# starting_rep = 1
+# ending_rep = 50
+# max_rep = 100
+# When this batch is finished, set
+# starting_rep = 51
+# ending_rep = 100
+# max_rep = 100
+starting_rep = 901 # should be > 0
+ending_rep = 1000
+max_rep = 1000 # note: changing this between batches will make results non-repeatable
+
+
+number_of_genes = 10
+number_of_species = 6
+gene_length = 2000
+
+fixedT = None # specify value if you wish to use the same T for every simulation;
+ # otherwise, specify None to draw T from a Lognormal distribution
+ # having mean T_mean and standard deviation T_sd (see below)
+T_mean = 0.05
+T_sd = 0.22
+
+thetaTratio_mean = 0.6
+thetaTratio_sd = 0.77
+
+
+# If True, each data file will be placed in its own subdirectory of outdir
+# along with a qsub script and python script suitable for conducting an MCMC
+# analysis using Phycas
+equip_for_cluster = True
+analysis_program = 'mrbayes'
+
+# MrBayes setup parameters
+ncycles = 3200000
+burnin = -2
+sample_every = 100
+report_every = 10000
+nstones = 30
+
+# If True, a plot will be created showing true gene trees embedded within the true species tree
+createplot = True
+
+# If True, the concatenated data set will also be analyzed for each set of genes
+concatenate = True
+
+# If True, simulate data sets using seqgen; if False, just simulate species and gene trees
+simulate_nucleotide_data = True
+
+use_fixed_species_tree = False
+fixed_tree_prescription = [(0.005,1,2),(0.005,5,3),(0.005,6,4)]
+
+# *********************************************************
+# ***** Should not need to modify anything below here *****
+# *********************************************************
+
+# Let:
+# Hg = species tree height in generations
+# T = species tree height in substitutions
+# Ht = species tree height in coalescent_units = Hg/(2 Ne)
+# theta = 3 Ne mu
+# mu = mutations per generation
+# s = speciations per generation; Yule (pure-birth) speciation rate
+#
+# IMPORTANT: specify only 2 of the 3 quantities (Ht, T, theta) below! The 3rd will be
+# calculated as follows:
+# Case 1: T and Ht specified
+# theta = 2 T/Ht
+# Case 2: T and theta specified
+# Ht = 2 T/theta
+# Case 3: Ht and theta specified
+# T = theta Ht/2
+#
+# If mimicking the Leaché and Rannala (2011) study (Systematic Biology 60:126–137),
+# specify theta and set Ht = 2T/theta, where theta and T are as defined in the
+# Leaché and Rannala paper:
+# Ht = 2T/theta
+# = 2(mu)(Hg)/(4 Ne mu)
+# = Hg/(2 Ne)
+# Note that Leaché and Rannala define Ne = twice number of diploid individuals,
+# whereas here Ne = number of diploids. That is, Ne* = 2 Ne, where Ne* is the
+# Leaché and Rannala definition and Ne is the definition used here.
+# For T = 0.016 and theta = 0.001 --> 64 Ne* (expect few deep coalescences)
+# set # Ht = 2*T/theta = 2*(0.016)/(0.001) = 32
+# For T = 0.001 and theta = 0.016 --> 0.25 Ne* (expect many deep coalescences)
+# set # Ht = 2*T/theta = 2*(0.001)/(0.016) = 0.125
+#
+# From Table 1 and Figure 2 in Leaché and Rannala. 2011. Syst. Biol. 60:126–137.
+# Notes: Ht = 2*T/theta, coaltime is height of tree in coalescent time units
+# T theta coaltime Ht var/1000 pr(deep coal.)
+# 0.016 0.001 64 Ne* 32 55.78 +- 7.36 low
+# 0.016 0.016 4 Ne* 2 86.92 +- 7.48 moderate
+# 0.001 0.001 4 Ne* 2 5.66 +- 0.51 moderate
+# 0.001 0.016 0.25 Ne* 0.125 35.45 +- 1.89 high
+# Specify 2 of the following 3 quantities, with 3rd specified as None
+# See below for explanation
+
+# Specify T, the height of the species tree in expected number of substitutions
+# This is identical to T in Leaché and Rannala (2011)
+T = None
+
+# Specify theta = 4 N_e mu
+theta = None
+
+# Specify the seed to use in generating the species tree, gene trees, and sequence data
+data_seed = None
+
+# Specify the seed to use for MCMC analyses
+analysis_seed = None
+
+# Specify name of directory to hold simulated data
+outdir = None
+
+# Specify Ht, the height of the species tree in coalescent units (2 N_e generations).
+Ht = None
+
+# Specify True if you want all genes to be simulated from the exact same gene tree
+# Specify False if you want data for each gene to be simulated from a different gene tree
+one_gene_tree = False
+
+nexus_template = '''#nexus
+
+begin data;
+ dimensions ntax=%d nchar=%d;
+ format datatype=dna missing=? gap=-;
+ matrix
+%s;
+end;
+
+'''
+paup_block = '''
+begin trees;
+ tree truth [TL = %.5f] [&U] = %s;
+end;
+
+begin paup;
+ set crit=like;
+ lset nst=2 tratio=estimate basefreq=estimate rates=gamma shape=estimate;
+ savetrees file=%s brlens;
+ [describe 1 / plot=phylogram brlen=sumonly;]
+end;
+'''
+
+def simulateSequences(gene_number, gene_tree_root, rnseed, scaling_factor = 1.0):
+ gene_tree_newick, TL = tree.makeNewick(gene_tree_root)
+
+ # create a temporary tree file that seq-gen will use
+ treef = tempfile.NamedTemporaryFile(mode='w', prefix='tmptree', delete=False)
+ treef.write(gene_tree_newick)
+ gene_tree_filename = treef.name
+ treef.close()
+
+ cmd = []
+ cmd.append(seqgen_location)
+ cmd.append('-mGTR') # use the GTR substitution model
+ cmd.append('-l%d' % gene_length) # sequence length (number of sites to simulate)
+ cmd.append('-n1') # number of data sets to simulate from this gene tree
+ cmd.append('-p1') # number of data partitions
+ cmd.append('-f0.25,0.25,0.25,0.25') # nucleotide frequencies
+ #cmd.append('-t2.0') # transition/transversion ratio (not the rate ratio!)
+ cmd.append('-r1.0,2.0,1.0,1.0,2.0,1.0') #
+ cmd.append('-a0.5') # shape parameter of gamma among-site rate heterogeneity
+ cmd.append('-z%d' % rnseed) # random number seed
+ cmd.append('-or') # output format (on=nexus, op=phylip, or=relaxed phylip)
+ cmd.append('-s%g' % scaling_factor) # branch length scaling factor
+ cmd.append(gene_tree_filename) # tree file name
+
+ if False:
+ cmd_filename = os.path.join(data_dir, 'sgcmd%d.sh' % gene_number)
+ cmdf = open(cmd_filename, 'w')
+ cmdf.write('#!/bin/bash\n\n')
+ cmdf.write(' '.join(cmd))
+ cmdf.write('\n')
+ cmdf.close()
+
+ phylip, error = subprocess.Popen(cmd, stdin=None, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
+
+ if False:
+ error_filename = os.path.join(data_dir, 'error%d.txt' % gene_number)
+ errorf = open(error_filename,'w')
+ errorf.write(error)
+ errorf.close()
+
+ # phylip holds the data matrix in relaxed-phylip format, with each line comprising
+ # a taxon name (here, just a number) followed by whitespace and then the sequence.
+ # The taxa appear in the order in which they are encountered in the tree file, so we
+ # reorder them and create taxon names using the pattern 'taxon%d' in the process of
+ # constructing the matrix
+ matrix_lines = phylip.strip().split('\n')
+ new_line = '\n'
+
+ # split matrix into lines and sort so that taxa are in order
+ data = {}
+ for line in matrix_lines[1:]:
+ if not re.match('\s*\[[- ]+\]\s*', line):
+ matchobj = re.match('\s*(\d+)\s+([ACGTacgt]+)\s*', line)
+ assert matchobj
+ taxon_number = int(matchobj.group(1))
+ seq = matchobj.group(2)
+ data[taxon_number] = seq
+ taxon_numbers = data.keys()
+ taxon_numbers.sort()
+ matrix = ''
+ for t in taxon_numbers:
+ taxon_name = 'taxon%d' % t
+ matrix += '%12s %s\n' % (taxon_name, data[t])
+
+ nexus = nexus_template % (number_of_species, gene_length, matrix)#, TL, gene_tree_newick, 'gene%d-hkyg.tre' % gene_number)
+
+ os.remove(gene_tree_filename)
+ return (data,nexus,gene_tree_newick)
+
+def createPartitionString():
+ partition_info = '';
+ first_site = 1;
+ last_site = gene_length
+ for gene in range(1, number_of_genes+1):
+ partition_info += ' charset gene%d = %6d - %6d;\n' % (gene, first_site, last_site);
+ first_site += gene_length
+ last_site += gene_length
+ partition_info += ' partition currentPartition = %d: %s;' % (number_of_genes, ', '.join(['gene%d' % gene for gene in range(1, number_of_genes+1)]))
+ return partition_info
+
+def equipForCluster(outdir, subdir, data_filename, nexus, analysis_seed, jobname):
+ global phycas_template, qsub_template, submitallf, galaxf
+
+ if equip_for_cluster:
+ # create gene-specific subdirectory within outdir
+ dir_path = os.path.join(outdir, subdir)
+ os.mkdir(dir_path)
+
+ # save data file
+ datafile_path = os.path.join(dir_path, data_filename)
+ nexusf = open(datafile_path, 'w')
+ nexusf.write(nexus)
+ nexusf.close()
+
+ if analysis_program == 'phycas':
+ # save phycas script
+ fn = os.path.join(dir_path, 'runphycas.py')
+ f = open(fn, 'w')
+ tmp = phycas_template.replace('__DATAFILENAME__', data_filename)
+ tmp = tmp.replace('__RNSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__NCYCLES__', repr(ncycles))
+ tmp = tmp.replace('__BURNIN__', repr(burnin))
+ tmp = tmp.replace('__SAMPLE_EVERY__', repr(sample_every))
+ tmp = tmp.replace('__REPORT_EVERY__', repr(report_every))
+ f.write(tmp)
+ f.close()
+
+ # save qsub script
+ fn = os.path.join(dir_path, 'qsub.sh')
+ f = open(fn, 'w')
+ f.write(qsub_template.replace('__JOBNAME__', jobname))
+ f.write(qsub_template.replace('__JOBCOMMAND__', "python runphycas.py"))
+ f.close()
+ elif analysis_program == 'mrbayes':
+ if subdir == 'concat_topo_brlen':
+ # save mrbayes script to handle partitioned (linked topology, unlinked brlens) case
+ fn = os.path.join(dir_path, 'runmb.nex')
+ f = open(fn, 'w')
+ tmp = mrbayes_topo_brlen_linked.replace('__DATAFILENAME__', data_filename)
+ tmp = tmp.replace('__RNSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__RNSWAPSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__NCYCLES__', repr(ncycles))
+ tmp = tmp.replace('__BURNIN__', repr(burnin))
+ tmp = tmp.replace('__NSTONES__', repr(nstones))
+ tmp = tmp.replace('__SAMPLE_EVERY__', repr(sample_every))
+ tmp = tmp.replace('__REPORT_EVERY__', repr(report_every))
+ tmp = tmp.replace('__PARTITION_CHARSETS__', createPartitionString())
+
+ f.write(tmp)
+ f.close()
+ elif subdir == 'concat_topo':
+ # save mrbayes script to handle partitioned (linked topology, unlinked brlens) case
+ fn = os.path.join(dir_path, 'runmb.nex')
+ f = open(fn, 'w')
+ tmp = mrbayes_topo_linked.replace('__DATAFILENAME__', data_filename)
+ tmp = tmp.replace('__RNSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__RNSWAPSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__NCYCLES__', repr(ncycles))
+ tmp = tmp.replace('__BURNIN__', repr(burnin))
+ tmp = tmp.replace('__NSTONES__', repr(nstones))
+ tmp = tmp.replace('__SAMPLE_EVERY__', repr(sample_every))
+ tmp = tmp.replace('__REPORT_EVERY__', repr(report_every))
+ tmp = tmp.replace('__PARTITION_CHARSETS__', createPartitionString())
+ f.write(tmp)
+ f.close()
+
+ else:
+ # save mrbayes script to handle unpartitioned case (concatenated or individual gene)
+ fn = os.path.join(dir_path, 'runmb.nex')
+ f = open(fn, 'w')
+ tmp = mrbayes_all_unlinked.replace('__DATAFILENAME__', data_filename)
+ tmp = tmp.replace('__RNSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__RNSWAPSEED__', repr(analysis_seed))
+ tmp = tmp.replace('__NCYCLES__', repr(ncycles))
+ tmp = tmp.replace('__BURNIN__', repr(burnin))
+ tmp = tmp.replace('__NSTONES__', repr(nstones))
+ tmp = tmp.replace('__SAMPLE_EVERY__', repr(sample_every))
+ tmp = tmp.replace('__REPORT_EVERY__', repr(report_every))
+ f.write(tmp)
+ f.close()
+
+ # save qsub script
+ fn = os.path.join(dir_path, 'qsub.sh')
+ f = open(fn, 'w')
+ tmp = qsub_template.replace('__JOBNAME__', jobname)
+ #tmp = tmp.replace('__JOBCOMMAND__', "mb runmb.nex")
+ #suman added
+ tmp = tmp.replace('__JOBCOMMAND__', "mb runmb.nex\nrm -f mcmc*.con.tre mcmc*.mcmc mcmc*.p mcmc*.parts mcmc*.trprobs mcmc*.tstat mcmc*.vstat mcmc*.ckp mcmc*.ckp~ ss*.*")
+
+ f.write(tmp)
+ f.close()
+ else:
+ assert False
+
+ # save line in submitall.sh script
+ submitallf.write('cd %s; qsub qsub.sh; cd ..\n' % (subdir,))
+
+ # save line in galax.txt file
+ if not subdir in ['concat_topo_brlen','concat_topo']:
+ if analysis_program == 'mrbayes':
+ galaxf.write('%s/mcmc.t\n' % subdir)
+ else:
+ galaxf.write('%s/trees.t\n' % subdir)
+ elif simulate_nucleotide_data:
+ # save data file
+ datafile_path = os.path.join(outdir, data_filename)
+ nexusf = open(datafile_path, 'w')
+ nexusf.write(nexus)
+ nexusf.close()
+
+def createConcatenated(data_dicts, species_tree_length, species_tree_newick):
+ taxon_numbers = data_dicts[0].keys()
+ taxon_numbers.sort()
+ matrix = ''
+ for t in taxon_numbers:
+ taxon_name = 'taxon%d' % t
+ concat_seq = ''
+ for d in data_dicts:
+ concat_seq += d[t]
+ matrix += '%12s %s\n' % (taxon_name, concat_seq)
+
+ nexus = nexus_template % (number_of_species, number_of_genes*gene_length, matrix)#, species_tree_length, species_tree_newick, 'concat-hkyg.tre')
+ return nexus
+
+def run(jobid, outdir, T, theta, data_seed, analysis_seed, scaling_factor, fake_it):
+ global phycas_template, mrbayes_all_unlinked, mrbayes_topo_linked, mrbayes_topo_brlen_linked, qsub_template, submitallf, galaxf
+
+ random.seed(data_seed)
+ rnseed = [random.randint(1,10000) for g in range(number_of_genes)]
+
+ assert theta is not None
+ assert T is not None
+
+ # Check to make sure user did not specify all three quantities (Ht, T, theta)
+ # ok = (theta is None) and (Ht is not None) and (T is not None)
+ # if not ok:
+ # ok = (theta is not None) and (Ht is None) and (T is not None)
+ # if not ok:
+ # ok = (theta is not None) and (Ht is not None) and (T is None)
+ # assert ok, 'Please specify only 2 of the 3 quantities: T, Ht, and theta'
+
+ # Calculate the 3rd quantity
+ # if theta is None:
+ # theta = 2.0*T/Ht
+ # elif T is None:
+ # T = theta*Ht/2.0
+ # else:
+ # Ht = 2.0*T/theta
+
+ # Calculate the mutation/speciation rate ratio
+ phi = yule.calcPhi(number_of_species)
+ mu_over_s = T/phi
+
+ if not fake_it:
+ output = 'outdir = %s\n' % outdir
+ output += 'master_seed = %d\n' % master_seed
+ output += 'data_seed = %d\n' % data_seed
+ output += 'analysis_seed = %d\n' % analysis_seed
+ output += 'number_of_genes = %d\n' % number_of_genes
+ output += 'number_of_species = %d\n' % number_of_species
+ output += 'gene_length = %d\n' % gene_length
+ output += 'theta = %g\n' % theta
+ output += 'T = %g\n' % T
+ output += '\n'
+
+ assert not os.path.exists(outdir), 'Output directory (%s) already exists; please delete or rename it and try again' % outdir
+ #shutil.rmtree(outdir)
+ os.mkdir(outdir)
+
+ if equip_for_cluster and simulate_nucleotide_data and not fake_it:
+ # read in contents of phycas_template.py
+ phycas_template = open('phycas_template.py','r').read()
+
+ # read in contents of mrbayes_all_unlinked.nex
+ mrbayes_all_unlinked = open('mrbayes_all_unlinked.nex','r').read()
+
+ # read in contents of mrbayes_topo_linked.nex
+ mrbayes_topo_linked = open('mrbayes_topo_linked.nex','r').read()
+
+ # read in contents of mrbayes_topo_brlen_linked.nex
+ mrbayes_topo_brlen_linked = open('mrbayes_topo_brlen_linked.nex','r').read()
+
+ # read in contents of qsub_template.sh
+ qsub_template = open('qsub_template.sh','r').read()
+
+ # Create submitall.sh file
+ submitallf = open(os.path.join(outdir,'submitall.sh'),'w')
+ submitallf.write('#!/bin/bash\n\n')
+
+ # Create galax.txt file
+ galaxf = open(os.path.join(outdir,'galax.txt'),'w')
+
+ if use_fixed_species_tree:
+ species_tree_root = fixed.fixedTree(number_of_species, fixed_tree_prescription)
+ else:
+ species_tree_root = yule.yuleTree(number_of_species, mu_over_s)
+ species_tree_height = tree.calcActualHeight(species_tree_root)
+ species_tree_newick, species_tree_length = tree.makeNewick(species_tree_root)
+
+ preorder = tree.getPreorder(species_tree_root)
+ postorder = preorder[:]
+ postorder.reverse()
+
+ epochs,msg = coal.calcEpochs(postorder, species_tree_height)
+ if not fake_it:
+ output += msg
+ total_deep_coalescences = 0
+ gene_tree_roots = []
+ gene_tree_heights = []
+ gene_tree_newicks = []
+ gene_data_dicts = []
+ for gene_index in range(number_of_genes):
+ gene_number = gene_index + 1
+ if gene_index == 0 or not one_gene_tree:
+ gene_tree_root = coal.coalTree(epochs, theta)
+ gene_tree_roots.append(gene_tree_root)
+
+ num_deep_coalescences = tree.countDeepCoalescences(gene_tree_root)
+ total_deep_coalescences += num_deep_coalescences
+
+ gene_tree_length = tree.sumEdgeLengths(gene_tree_root)
+ gene_tree_height = tree.calcActualHeight(gene_tree_root)
+ gene_tree_heights.append(gene_tree_height)
+
+ if not fake_it:
+ output += 'gene %d --> deep = %d --> tree height = %.5f (%.1f times higher than species tree)\n' % (gene_index, num_deep_coalescences, gene_tree_height, gene_tree_height/species_tree_height)
+
+ # Run seqgen to generate sequence data on this gene tree
+ if simulate_nucleotide_data and not fake_it:
+ gene_seqs, nexus, newick = simulateSequences(gene_number, gene_tree_root, rnseed[gene_index], scaling_factor)
+ gene_tree_newicks.append(newick)
+ gene_data_dicts.append(gene_seqs)
+
+ gene_subdir = 'gene%d' % gene_number
+ gene_data_filename = 'gene%d.nex' % gene_number
+ jobname = 'r%sg%d' % (jobid,gene_number)
+ equipForCluster(outdir, gene_subdir, gene_data_filename, nexus, analysis_seed, jobname)
+
+ if concatenate and simulate_nucleotide_data and not fake_it:
+ nexus = createConcatenated(gene_data_dicts, species_tree_length, species_tree_newick)
+ concat_subdir = 'concat_topo_brlen'
+ concat_data_filename = 'concatenated.nex'
+ jobname = 'r%scat' % jobid
+ equipForCluster(outdir, concat_subdir, concat_data_filename, nexus, analysis_seed, jobname)
+
+ if analysis_program == 'mrbayes':
+ # create second concatenated directory for performing the analysis in which
+ # topology is linked but brlens are unlinked across genes
+ concat_subdir = 'concat_topo'
+ concat_data_filename = 'concatenated.nex'
+ jobname = 'r%scatp' % jobid
+ equipForCluster(outdir, concat_subdir, concat_data_filename, nexus, analysis_seed, jobname)
+
+ # Create tree file containing the species tree and all gene trees
+ if not fake_it:
+ treef = open(os.path.join(outdir,'species_and_gene_trees.tre'),'w')
+ treef.write('#nexus\n\n')
+ treef.write('begin trees;\n')
+ treef.write(' translate\n')
+ for t in range(1, number_of_species+1):
+ comma = ','
+ if t == number_of_species:
+ comma = ' '
+ taxon_name = 'taxon%d' % t
+ treef.write(' %d %s%s\n' % (t, taxon_name, comma))
+ treef.write(' ;\n')
+ treef.write('tree species_tree = [&U] %s;\n' % species_tree_newick)
+ for i,gene_tree_newick in enumerate(gene_tree_newicks):
+ treef.write('tree gene%d = [&U] %s;\n' % (i+1,gene_tree_newick))
+ treef.write('end;\n')
+ treef.close()
+
+ deepcoal_per_gene = float(total_deep_coalescences)/number_of_genes
+ deepcoal_per_gene_per_epoch = deepcoal_per_gene/(number_of_species - 1)
+ avgHeightRatio = sum(gene_tree_heights)/(species_tree_height*len(gene_tree_heights))
+ if not fake_it:
+ output += '\n'
+ output += 'Total deep coalescences = %d\n' % total_deep_coalescences
+ output += 'Maximum possible number of deep coalescences = %d*(%d - 1) = %d\n' % (number_of_genes, number_of_species, number_of_genes*(number_of_species-1))
+ output += 'No. deep coalescences/gene = %g\n' % deepcoal_per_gene
+ output += 'No. deep coalescences/gene/epoch = %g\n' % deepcoal_per_gene_per_epoch
+ output += 'Species tree height = %.5f\n' % species_tree_height
+ output += 'Shortest gene tree = %.5f\n' % min(gene_tree_heights)
+ output += 'Tallest gene tree = %.5f\n' % max(gene_tree_heights)
+ output += 'Gene trees were on average %.1f times higher than the species tree\n' % avgHeightRatio
+ print output
+ outf = open(os.path.join(outdir,'gist-output.txt'),'w')
+ outf.write(output)
+ outf.close()
+
+ if createplot:
+ # flip = True is needed if you want to compare plot with trees in species_and_gene_trees.tre
+ # using FigTree
+ plot.geneTreesWithinSpeciesTree(os.path.join(outdir,'species_and_gene_trees.pdf'), species_tree_root, gene_tree_roots, flip=True)
+
+ return (total_deep_coalescences, avgHeightRatio)
+
+def logNormalParamsFromMeanAndStdev(mean, sd):
+ # computes mu and sigma (mean and standard deviation on log scale) from the
+ # supplied mean and standard deviation on the linear scale
+ variance = math.pow(sd, 2.0)
+ meansq = math.pow(mean, 2.0)
+ sigmasq = math.log(1.0 + variance/meansq)
+ mu = math.log(mean) - sigmasq/2.0
+ sigma = math.sqrt(sigmasq)
+ return (mu, sigma)
+
+def random_theta_T(T_values, theta_values, params):
+ if os.path.exists(top_level_dir):
+ shutil.rmtree(top_level_dir)
+ os.mkdir(top_level_dir)
+
+ gosh = open('%s/go.sh' % top_level_dir, 'w')
+ gosh.write('#!/bin/bash\n')
+
+ qsubgalaxsh = open('%s/qsub_galax.sh' % top_level_dir, 'w')
+ qsubgalaxsh.write('#!/bin/bash\n')
+ qsubgalaxsh.write('#$ -S /bin/bash\n')
+ qsubgalaxsh.write('#$ -cwd\n')
+ qsubgalaxsh.write('#$ -N bfpy\n')
+ qsubgalaxsh.write('#$ -q highpri.q,highmem.q\n')
+ qsubgalaxsh.write('python bayes_factors.py\n')
+ qsubgalaxsh.write('python galax_pairs.py\n')
+ qsubgalaxsh.write('\n')
+ os.fchmod(qsubgalaxsh.fileno(), stat.S_IRWXU) # change permissions before closing
+ qsubgalaxsh.close()
+
+ summpy = open('%s/bayes_factors.py' % top_level_dir, 'w')
+ summpy.write(open('bayes_factors.py','r').read())
+ summpy.close()
+
+ galaxpairspy = open('%s/galax_pairs.py' % top_level_dir, 'w')
+ galaxpairspy.write(open('galax_pairs.py','r').read())
+ galaxpairspy.close()
+
+ #summsh = open('%s/summ.sh' % top_level_dir, 'w')
+ #summsh.write('#!/bin/bash\n')
+ #summsh.write('export S="summarize.py"\n')
+
+ assert max_rep == len(T_values)
+ assert max_rep == len(theta_values)
+ assert max_rep == len(params)
+ data_seeds = [random.randint(1,10000) for i in range(max_rep)]
+ a_seeds = [random.randint(1,10000) for i in range(max_rep)]
+ b_seeds = [random.randint(1,10000) for i in range(max_rep)]
+
+ all_combos = zip(T_values,theta_values)
+ rep = 1
+ for T,theta in all_combos:
+ if rep > max_rep:
+ break
+
+ if rep < starting_rep or rep > ending_rep:
+ fake_it = True
+ else:
+ fake_it = False
+
+ seed_index = rep - 1
+
+ print 'rep = %d, theta = %g, T = %g, theta/T = %g' % (rep, theta, T, theta/T)
+
+ data_seed = data_seeds[seed_index]
+ #summsh.write('python $S run%d\n' % rep)
+
+ analysis_seed = a_seeds[seed_index]
+ jobid = '%da' % rep
+ outdir = '%s/run%s' % (top_level_dir, jobid)
+ if not fake_it:
+ gosh.write('\ncd run%da\n. submitall.sh\ncd ..\n' % rep)
+ scaler = 1.0
+ deepcoal1, avg_height_ratio1 = run(jobid, outdir, T, theta, data_seed, analysis_seed, scaler, fake_it)
+
+ analysis_seed = b_seeds[seed_index]
+ jobid = '%db' % rep
+ outdir = '%s/run%s' % (top_level_dir, jobid)
+ if not fake_it:
+ gosh.write('\ncd run%db\n. submitall.sh\ncd ..\n' % rep)
+ deepcoal2, avg_height_ratio2 = run(jobid, outdir, T, theta, data_seed, analysis_seed, scaler, fake_it)
+
+ assert deepcoal1 == deepcoal2
+ assert avg_height_ratio1 == avg_height_ratio2
+ params[seed_index]['deep'] = deepcoal1
+ params[seed_index]['ratio'] = avg_height_ratio1
+
+ rep += 1
+
+ # change the permissions on the go.sh script so that user has read, write, and execute permssions
+ # S_IRWXU --> Read, Write, eXecute for User
+ # fileno() --> returns open file handle
+ os.fchmod(gosh.fileno(), stat.S_IRWXU)
+ gosh.close()
+
+ paramf = open('%s/parameter_values.txt' % top_level_dir, 'w')
+ paramf.write('%12s %12s %12s %12s %12s\n' % ('rep', 'T', 'theta/T', 'deep', 'ratio'))
+ for i in range(starting_rep-1,ending_rep):
+ paramf.write('%12d %12.5f %12.5f %12.5f %12.5f\n' % (i+1, params[i]['T'], params[i]['theta/T'], params[i]['deep'], params[i]['ratio']))
+ paramf.close()
+
+ deep = []
+ max_deep = number_of_genes*(number_of_species-1)
+ nbins = 5
+ upper_bin_boundary = [float(x+1)*max_deep/nbins for x in range(nbins)]
+ namesarg = ["%.1f-%.1f" % ((float(x)*max_deep/nbins),(float(x+1)*max_deep/nbins)) for x in range(nbins)]
+ bin_count = [0]*nbins
+ for i in range(max_rep):
+ for k,u in enumerate(upper_bin_boundary):
+ if params[i]['deep'] < u:
+ bin_count[k] += 1
+ break
+ deephistf = open('%s/deep_hist.R' % top_level_dir, 'w')
+ deephistf.write('# sum of bin counts = %g\n' % sum(bin_count))
+ deephistf.write('deep = c(%s)\n' % ','.join(['%d' % b for b in bin_count]))
+ deephistf.write('barplot(deep, names.arg=c(%s))\n' % ','.join('"%s"' % nm for nm in namesarg))
+ deephistf.close()
+
+if __name__ == '__main__':
+ random.seed(master_seed)
+
+ Tmu, Tsigma = logNormalParamsFromMeanAndStdev(T_mean, T_sd)
+
+ print
+ print 'T mean =', T_mean
+ print 'T sd =', T_sd
+ print '--> Lognormal(mu=%.5f, sigma=%.5f)' % (Tmu,Tsigma)
+
+ Rmu, Rsigma = logNormalParamsFromMeanAndStdev(thetaTratio_mean, thetaTratio_sd)
+
+ print
+ print 'theta/T ratio mean =', thetaTratio_mean
+ print 'theta/T ratio sd =', thetaTratio_sd
+ print '--> Lognormal(mu=%.5f, sigma=%.5f)' % (Rmu,Rsigma)
+
+ T_values = []
+ theta_values = []
+ params = []
+ for i in range(max_rep):
+ if fixedT is None:
+ T = random.lognormvariate(Tmu, Tsigma)
+ else:
+ T = fixedT
+
+ T_values.append(T)
+
+ q_over_T = random.lognormvariate(Rmu, Rsigma)
+ q = T*q_over_T
+ theta_values.append(q)
+
+ params.append({'T':T, 'theta':q, 'theta/T':q/T})
+
+ random_theta_T(T_values, theta_values, params)
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_all_unlinked.nex b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_all_unlinked.nex
new file mode 100644
index 0000000..850c555
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_all_unlinked.nex
@@ -0,0 +1,12 @@
+#nexus
+
+begin mrbayes;
+ exe __DATAFILENAME__;
+ Lset nst=6 rates=gamma ngammacat=4;
+ Prset brlenspr=unconstrained:GammaDir(1.0, 0.1,1.0, 1.0) shapepr=exp(1.0) Revmatpr=dirichlet(10.0,20.0,10.0,10.0,20.0,10.0) statefreqpr=dirichlet(10.0,10.0,10.0,10.0);
+ set seed=__RNSEED__ swapseed=__RNSWAPSEED__;
+ mcmc ngen=__NCYCLES__ samplefreq=__SAMPLE_EVERY__ printfreq=__REPORT_EVERY__ nchains=1 nruns=1 filename=mcmc;
+ sumt filename=mcmc;
+ mcmcp filename=ss;
+ ss alpha=0.3 nsteps=__NSTONES__ burninss=__BURNIN__ ;
+end;
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_topo_brlen_linked.nex b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_topo_brlen_linked.nex
new file mode 100644
index 0000000..1b691e1
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_topo_brlen_linked.nex
@@ -0,0 +1,20 @@
+#nexus
+
+begin mrbayes;
+ exe __DATAFILENAME__;
+
+__PARTITION_CHARSETS__
+
+ set partition = currentPartition;
+ unlink statefreq=(all) revmat=(all) shape=(all);
+
+ Lset applyto=(all) nst=6 rates=gamma ngammacat=4;
+ Prset applyto=(all) brlenspr=unconstrained:GammaDir(1.0, 0.1,1.0, 1.0) shapepr=exp(1.0) Revmatpr=dirichlet(10.0,20.0,10.0,10.0,20.0,10.0) statefreqpr=dirichlet(10.0,10.0,10.0,10.0);
+ prset ratepr=dirichlet(10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0);
+
+ set seed=__RNSEED__ swapseed=__RNSWAPSEED__;
+ mcmc ngen=__NCYCLES__ samplefreq=__SAMPLE_EVERY__ printfreq=__REPORT_EVERY__ nchains=1 nruns=1 filename=mcmc;
+ sumt filename=mcmc;
+ mcmcp filename=ss;
+ ss alpha=0.3 nsteps=__NSTONES__ burninss=__BURNIN__ ;
+end;
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_topo_linked.nex b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_topo_linked.nex
new file mode 100644
index 0000000..9ef57b1
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/mrbayes_topo_linked.nex
@@ -0,0 +1,19 @@
+#nexus
+
+begin mrbayes;
+ exe __DATAFILENAME__;
+
+__PARTITION_CHARSETS__
+
+ set partition = currentPartition;
+ unlink statefreq=(all) revmat=(all) shape=(all) brlens=(all);
+
+ Lset applyto=(all) nst=6 rates=gamma ngammacat=4;
+ Prset applyto=(all) brlenspr=unconstrained:GammaDir(1.0, 0.1,1.0, 1.0) shapepr=exp(1.0) Revmatpr=dirichlet(10.0,20.0,10.0,10.0,20.0,10.0) statefreqpr=dirichlet(10.0,10.0,10.0,10.0);
+
+ set seed=__RNSEED__ swapseed=__RNSWAPSEED__;
+ mcmc ngen=__NCYCLES__ samplefreq=__SAMPLE_EVERY__ printfreq=__REPORT_EVERY__ nchains=1 nruns=1 filename=mcmc;
+ sumt filename=mcmc;
+ mcmcp filename=ss;
+ ss alpha=0.3 nsteps=__NSTONES__ burninss=__BURNIN__ ;
+end;
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/phycas_template.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/phycas_template.py
new file mode 100644
index 0000000..46232f7
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/phycas_template.py
@@ -0,0 +1,93 @@
+from phycas import *
+
+setMasterSeed(__RNSEED__)
+
+# Set up HKY model
+model.type = 'hky'
+model.kappa_prior = Exponential(1.0)
+model.state_freq_prior = Dirichlet((1.0, 1.0, 1.0, 1.0))
+
+# Assume no invariable sites
+model.pinvar_model = False
+
+# Add discrete gamma rate heterogeneity submodel
+model.num_rates = 4
+model.gamma_shape_prior = Exponential(1.0)
+
+# Use independent exponential priors (mean 0.1) for each edge length parameter
+model.edgelen_prior = Exponential(10.0)
+model.edgelen_hyperprior = InverseGamma(2.10000, 0.90909)
+
+mcmc.data_source = '__DATAFILENAME__'
+
+# Conduct a Markov chain Monte Carlo (MCMC) analysis that samples from the posterior distribution
+mcmc.ncycles = __NCYCLES__
+mcmc.burnin = __BURNIN__
+mcmc.target_accept_rate = 0.3
+mcmc.sample_every = __SAMPLE_EVERY__
+mcmc.report_every = __REPORT_EVERY__
+#mcmc.starting_tree_source = TreeCollection(newick='(1:.01,2:0.01,(3:0.01,4:0.01):0.01)')
+#mcmc.starting_tree_source = TreeCollection(filename='nexustreefile.tre')
+mcmc.fix_topology = False
+mcmc.allow_polytomies = False
+mcmc.bush_move_weight = 0
+mcmc.ls_move_weight = 100
+mcmc.out.log = 'mcmcoutput.txt'
+mcmc.out.log.mode = REPLACE
+mcmc.out.trees = 'trees.t'
+mcmc.out.trees.mode = REPLACE
+mcmc.out.params = 'params.p'
+mcmc.out.params.mode = REPLACE
+mcmc()
+
+# Summarize the posterior distribution of tree topologies and clades
+sumt.trees = 'trees.t'
+sumt.skip = 1
+sumt.tree_credible_prob = 0.95
+sumt.save_splits_pdf = False
+sumt.save_trees_pdf = True
+sumt.out.log.prefix = 'sumt-log'
+sumt.out.log.mode = REPLACE
+sumt.out.trees.prefix = 'sumt-trees'
+sumt.out.trees.mode = REPLACE
+sumt.out.splits.prefix = 'sumt-splits'
+sumt.out.splits.mode = REPLACE
+sumt()
+
+# Estimate the marginal likelihood using the Generalized Stepping-stone (GSS) method
+# Estimate the reference distribution to use with Generalized SS
+refdist.skip = 1
+refdist.params = 'params.p'
+refdist.trees = 'trees.t'
+refdist.out.refdistfile = 'refdist.txt'
+refdist.out.refdistfile.mode = REPLACE
+refdist()
+
+# Do not fix the tree topology for stepping-stone analysis
+mcmc.fix_topology = False
+
+# Choose different output file names to avoid overwriting the ones used for the reference distribution
+mcmc.out.log = 'ss-output.txt'
+mcmc.out.log.mode = REPLACE
+mcmc.out.trees = 'ss-trees.t'
+mcmc.out.trees.mode = REPLACE
+mcmc.out.params = 'ss-params.p'
+mcmc.out.params.mode = REPLACE
+# Set up and run the ss command (this will make use of many mcmc settings)
+ss.nstones = 20
+ss.ncycles = 500
+ss.sample_every = 1
+ss.report_every = 100
+ss.refdist_is_prior = False
+ss.refdistfile = 'refdist.txt'
+ss.shape1 = 1.000000
+ss.shape2 = 1.000000
+ss()
+
+# Running sump on the param file output by the ss command will calculate the marginal likelihood estimate
+sump.file = 'ss-params.p'
+sump.skip = 1
+sump.out.log.prefix = 'ss-sump-log'
+sump.out.log.mode = REPLACE
+sump()
+
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plot.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plot.py
new file mode 100644
index 0000000..d13dbbd
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plot.py
@@ -0,0 +1,163 @@
+import tree
+
+import matplotlib
+matplotlib.use('PDF')
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import matplotlib.cm as cm
+import matplotlib.patches as patches
+import matplotlib.font_manager as fm
+
+def leftmostDescendant(nd):
+ if nd.lchild is None:
+ return nd.number
+ nextnd = nd.lchild
+ leftmost = nextnd.number
+ while nextnd.lchild is not None:
+ nextnd = nextnd.lchild
+ leftmost = nextnd.number
+ return leftmost
+
+def rightmostDescendant(nd):
+ if nd.lchild is None:
+ return nd.number
+ nextnd = nd.lchild.rsib
+ rightmost = nextnd.number
+ while nextnd.lchild is not None:
+ nextnd = nextnd.lchild.rsib
+ rightmost = nextnd.number
+ return rightmost
+
+def plotSpeciesTree(ax1, root, ls, lw, nd = None):
+ if nd is None:
+ nd = root
+
+ if nd.lchild is not None:
+ plotSpeciesTree(ax1, root, ls, lw, nd.lchild)
+
+ if nd.lchild is None:
+ nd.x = 0.0
+ nd.y = ycoord4taxon[nd.number]
+ else:
+ yleft = rightmostDescendant(nd.lchild)
+ yright = leftmostDescendant(nd.lchild.rsib)
+ nd.y = (ycoord4taxon[yleft] + ycoord4taxon[yright])/2.0
+ # print '~~> node %d x = %g' % (nd.number, nd.x)
+ ax1.plot([nd.x, 0.0], [nd.y, nd.y], ls, linewidth=lw, solid_capstyle="butt")
+
+ if nd.par is not None:
+ nd.par.x = nd.x + nd.edgelen
+
+ if nd.rsib is not None:
+ plotSpeciesTree(ax1, root, ls, lw, nd.rsib)
+
+def plotGeneTree(ax1, root, color_index, lw, nd = None):
+ #global next_tip
+
+ if nd is None:
+ #next_tip = 0
+ nd = root
+ #done = False
+
+ if nd.lchild is not None:
+ plotGeneTree(ax1, root, color_index, lw, nd.lchild)
+
+ if nd.lchild is None:
+ nd.x = 0.0
+
+ # yjigger spaces out tips of different gene trees so that they do not overlap one another
+ if number_of_genes == 1:
+ yjigger = 0.0
+ else:
+ yjigger = 0.5*color_index/(number_of_genes - 1) - 0.25
+ nd.y = ycoord4taxon[nd.number] + yjigger # next_tip
+
+ #next_tip += 1
+ # print 'tip: x = %g, y = %g | number = %d' % (nd.x, nd.y, nd.number)
+ ax1.plot([nd.x, nd.x + nd.edgelen], [nd.y, nd.y], color=colors[color_index], linestyle="-", linewidth=lw)
+ else:
+ nd.y /= 2.0
+ # print 'int: x = %g, y = %g | number = %d' % (nd.x, nd.y, nd.number)
+ ax1.plot([nd.x, nd.x + nd.edgelen], [nd.y, nd.y], color=colors[color_index], linestyle="-", linewidth=lw, zorder=0)
+ ax1.plot([nd.x, nd.x], [nd.lchild.y, nd.lchild.rsib.y], color=colors[color_index], linestyle="-", linewidth=lw, zorder=0)
+
+ if nd.deep:
+ radius = .1
+ ellipse = patches.Ellipse((nd.x, nd.y), radius*w_to_h_ratio, radius, color='r', zorder=1)
+ ax1.add_artist(ellipse)
+
+ if nd.par is not None:
+ nd.par.x = nd.x + nd.edgelen
+ if nd.par.lchild == nd:
+ nd.par.y = nd.y
+ else:
+ nd.par.y += nd.y
+
+ if nd.rsib is not None:
+ plotGeneTree(ax1, root, color_index, lw, nd.rsib)
+
+def calcMaxGeneTreeHeight(gene_tree_roots):
+ max_gene_tree_height = 0.0
+ for gene_tree_root in gene_tree_roots:
+ h = tree.calcActualHeight(gene_tree_root)
+ if h > max_gene_tree_height:
+ max_gene_tree_height = h
+ return max_gene_tree_height
+
+def geneTreesWithinSpeciesTree(pdf_file_name, species_tree_root, gene_tree_roots, flip=False):
+ global colors, ycoord4taxon, number_of_genes, w_to_h_ratio
+
+ number_of_genes = len(gene_tree_roots)
+ colors = cm.rainbow(np.linspace(0, 1, number_of_genes))
+
+ fig = plt.figure()
+ gs = gridspec.GridSpec(1, 1)
+ ax1 = plt.subplot(gs[0])
+ #ax1.set_frame_on(False)
+ #ax1.get_yaxis().set_visible(False)
+ ax1.set_xlabel('Time (substitutions/site)')
+ ax1.set_ylabel('')
+
+ total_generations = calcMaxGeneTreeHeight(gene_tree_roots)
+
+ xlen = 1.05*total_generations
+ ax1.set_xlim(-0.025*total_generations, 1.025*total_generations)
+
+ number_of_species = 0
+ tree.flipAllNodesAbove(species_tree_root)
+ preorder = tree.getPreorder(species_tree_root)
+ for nd in preorder:
+ if nd.lchild is None:
+ number_of_species += 1
+
+ ylen = float(number_of_species + 2)
+ ax1.set_ylim(-1.,number_of_species)
+
+ w_to_h_ratio = xlen/ylen
+
+ ycoord4taxon = {}
+ y_ticks = []
+ y_axis_values = []
+ i = 0
+ for nd in preorder:
+ if nd.lchild is None:
+ ycoord4taxon[nd.number] = i
+ y_axis_values.append('%d' % nd.number)
+ y_ticks.append(float(i))
+ i += 1
+
+ # supply our own tick labels for the y axis
+ ax1.set_yticks(y_ticks)
+ fp = fm.FontProperties(family="sans-serif", size="xx-small")
+ ax1.set_yticklabels(y_axis_values, fontproperties=fp)
+
+ # hide tick marks for y axis
+ ax1.tick_params(axis='y', length=0)
+
+ plotSpeciesTree(ax1, species_tree_root, 'y-', 5)
+ for i,gene_tree_root in enumerate(gene_tree_roots):
+ tree.flipAllNodesAbove(gene_tree_root)
+ plotGeneTree(ax1, gene_tree_root, i, 1)
+
+ plt.savefig(pdf_file_name,format='pdf')
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plot.pyc b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plot.pyc
new file mode 100644
index 0000000..a3d77d3
Binary files /dev/null and b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plot.pyc differ
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.DS_Store b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.DS_Store differ
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.Rapp.history b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.Rapp.history
new file mode 100644
index 0000000..891a2a0
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.Rapp.history
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+logx = log(lsi)
+qqplot(logx)
+qexp(.95, rate=.1)
+log(30)-(30**2/2)
+2**3
+log(30)-((30**2)/2)
+qlnorm(.95, meanlog=-450, sdlog = 30)
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+5000,-101.09000,-19.14000,1.06000,-97.56000,-11.46000,-97.46000,-41.25000,-0.17000,-213.95000,-270.96000,-80.15000,-49.78000,5.21000,12.48000,-39.08000,-98.80000,-384.03000,-11.27000,-81.15000,-12.71000,-82.72000,-184.58000,-155.39000,-14.41000,-12.93000,-38.57000,-2.07000,-31.05000,-12.48000,-23.79000,-5.44000,-384.60000,-13.64000,-480.73000,-49.75000,-220.36000,-36.86000,-36.14000,5.07000,-142.86000,-27.27000,-104.11000,-73.90000,-79.81000,-21.69000,-31.18000,-408.70000,-158.14000,-107.82000,-17.26000,0.29000,-11.09000,-233.06000,-151.87000,-77.11000,-38.55000,-188.94000,0.69000,-95.55000,-76.22000,-1.40000,-41.07000,-68.81000,-53.13000,-5.46000,-7.81000,-83.09000,-4.13000,-36.00000,-5.56000,-3.81000,-93.80000,-31.82000,-27.08000,-63.23000,-102.60000,-34.17000,-3.72000,-51.61000,-490.64000)#
+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[100])#
+sprintf("high = %.5f",b[101])
+test <- c(0.7169867889377576, 1.272562393111007, 0.07836291079807645, 0.12776310030524826, 1.2660271415744553, 0.7222819850166892, 0.3752826753032609, 0.14582398314207995, 0.033427225913761946, 0.5290996422528664, 0.08277059173107155, 0.34505177895808503, 0.6885090860761939, 0.6169791095085385, 1.1408894110205123, 0.3065067378503016, 0.6610119603572205, 1.314263820830532, 0.5549635191787536, 0.24613706129066715, 0.08833568338483001, 0.10576432067626838, 0.5166417529486029, 0.013640478784471513, 1.2373403408928336, 0.582882315730692, 0.18821255760514224, 0.4302211803326113, 1.5838819891123186, 0.2916550786121929, 0.08074750972322199, 0.43741628323906795, 0.42984071678074137, 0.012426026640127156, 0.4089127268577747, 0.7897025536157075, 0.1773735695295365, 0.1138196246822811, 0.4314006011556393, 0.04596844941476312, 0.3301917598681176, 0.2456429356584802, 0.34153156871660856, 0.2550815587921409, 1.5149831056058871, 0.16603451696672308, 0.7395052224825868, 0.0397904442843754, 0.6792181466641348, 0.218707506747
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+097459146)
+test
+density(test)
+yl <- density(test)
+plot(y1)
+plot(yl)
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+gh <- sort(bdiff)
+gh
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+z90 = c(21,21,21,24,21,21,24,21,24,21,21,21,21,24,24,21,24,21,24,21,21,22,24,24,21,21,21,24,21,21,21,24,24,21,24,24,21,24,24,24,21,24,21,24,22,21,24,21,21,24,24,24,21,24,22,24,24,24,21,24,21,24,21,24,21,21,24,21,24,24,24,21,24,21,21,21,21,22,24,24,21,24,24,21,24,21,21,24,21,21,21,24,22,21,21,24,21,21,21,24,21,24,21,21,21,21,24,21,24,21,21,24,22,24,21,24,21,24,21,21,24,21,21,24,21,24,21,21,21,24,21,24,21,21,22,24,21,24,24,24,21,24,22,21,24,21,24,21,21,24,21,24,21,22,24,22,21,24,21,21,21,24,21,21,24,21,24,24,24,21,21,24,24,21,24,21,21,24,21,21,24,21,21,24,21,24,24,24,24,24,21,21,21,21,24,24,21,24,22,24,21,24,24,21,24,21,24,24,21,21,21,24,21,24,24,21,22,21,21,21,24,22,24,24,24,24,24,21,21,24,24,21,21,24,21,21,24,24,24,24,21,21,24,24,24,24,21,21,24,24,24,24,24,21,24,21,24,21,24,22,22,21,24,21,24,24,21,21,24,21,24,24,21,21,21,24,21,24,21,22,24,21,24,24,22,22,24,21,24,21,21,21,21,24,21,21,24,24,21,24,24,21,24,22,21,24,21,22,21,22,24,21,24,24,24,21,21,24,24,24,24,21,21,24,21,21,22,21,21,21,21,24,24,21,21,24,21,21,
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+pdf("a-Xdeep-Yd.pdf")#
+plot(x,y,type="p",pch=z90,col=c,bg=bgc,main="",xlab="Number of deep coalescences",ylab="D")#
+dev.off()
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+b <- sort(c(bsame1, bdiff1, bsame2, bdiff2, bsame3, bdiff3))#
+plot(density(bsame), col="blue", main = 'low = -22.39, high = -22.35', xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[3000])#
+sprintf("high = %.5f",b[3001])#
+#
+abline(v= -22.37,lty=3)#
+axis(3, at =1 ,labels='c = -22.37')
+bsame <- c(-2768.82000,-3281.64000,-3219.40000,-2828.25000,-4770.72000,-4882.24000,-1577.20000,-1790.59000,-2234.19000,-2115.97000,-2347.84000,-1402.35000,-3881.33000)#
+bdiff <- c(-2768.82000,-3281.64000,-3219.40000,-2828.25000,-4770.72000,-4882.24000,-1577.20000,-1790.59000,-2234.19000,-2115.97000,-2347.84000,-1402.35000,-3881.33000)#
+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])
+bsame <- c(-2768.82000,-3281.64000,-3219.40000,-2828.25000,-4770.72000,-4882.24000,-1577.20000,-1790.59000,-2234.19000,-2115.97000,-2347.84000,-1402.35000,-3881.33000)#
+bdiff <- c(-2.82000,-5.64000,-7.40000,5.25000,2.72000,1.24000,1.20000,1.59000,1.19000,2.97000,-2.84000,-1.35000,-3.33000)#
+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])
+bsame <- c(-2768.82000,-3281.64000,-3219.40000,-2828.25000,-4770.72000,-4882.24000,-1577.20000,-1790.59000,-2234.19000,-2115.97000,-2347.84000,-1402.35000,-3881.33000)#
+bdiff <- c(-2.82000,-5.64000,-7.40000,5.25000,2.72000,1.24000,1.20000,1.59000,1.19000,2.97000,-2.84000,-1.35000,-3.33000)#
+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[13])#
+sprintf("high = %.5f",b[14])
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+3000,-3297.66000,-4250.94000,-3491.63000,-4627.74000,-1608.55000,-169.69000,-608.74000,-2640.37000,-2918.51000,-1413.76000,-3276.37000,-1384.77000,-1382.89000,-4070.35000,-1980.08000,-5069.74000,-550.89000,-2874.50000,-2150.51000,-3144.71000,-3134.44000,-5144.22000,-3105.92000,-816.73000,-891.10000,-4642.80000,-2848.76000,-3646.11000,-4543.62000,-5635.14000,-4764.78000,-3241.78000,-2964.21000,-5761.93000,-2372.73000,-1610.51000,-4713.94000,-4110.37000,-2174.54000,-2333.20000,-3310.67000,-2514.85000,-1754.15000,-4029.24000,-3780.71000,-5121.93000,-1425.28000,-2346.24000,-2811.82000,-2482.02000,-1281.97000,-4537.35000,-3433.70000,-1728.19000)#
+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
+abline(v=-1.52, lty = 2, untf = FALSE)#
+axis(3, at =1 ,labels='c = -1.52',cex.axis=1.50)
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", cex.axis=1.50, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
+abline(v=-1.52, lty = 2, untf = FALSE)#
+axis(3, at =1 ,labels='c = -1.52',cex.axis=1.50)
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", cex.lab=1.50, cex.axis=1.10, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
+abline(v=-3.2, lty = 2, untf = FALSE)#
+axis(3, at =1 ,labels='c = −3.2',cex.axis=1.50)
+setwd("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/hky")
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", cex.lab=1.50, cex.axis=1.10, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
+abline(v=-3.2, lty = 2, untf = FALSE)#
+axis(3, at =1 ,labels='c = −3.2',cex.axis=1.50)
+pdf("BF_calibration_6taxa_10genes_hky.pdf")#
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", cex.lab=1.50, cex.axis=1.10, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
+abline(v=-3.2, lty = 2, untf = FALSE)#
+axis(3, at =1 ,labels='c = −3.2',cex.axis=1.50)#
+dev.off()
+pdf("BF_calibration_6taxa_10genes_hky.pdf")#
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", cex.lab=1.50, cex.axis=1.10, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
+abline(v=-3.2, lty = 3)#
+axis(3, at =1 ,labels='c = −3.2',cex.axis=1.50)#
+dev.off()
+pdf("6taxa.pdf")#
+bsame1 <- 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+#
+bsame <- c(bsame1, bsame2, bsame3)#
+bdiff <- c(bdiff1, bdiff2, bdiff3)#
+#
+b <- sort(c(bsame1, bdiff1, bsame2, bdiff2, bsame3, bdiff3))#
+plot(density(bsame), col="blue", cex.lab=1.50, cex.axis=1.10, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[3000])#
+sprintf("high = %.5f",b[3001])#
+#
+abline(v= -22.37, lty=3)#
+axis(3, at =1 ,labels='c = -22.37',cex.axis=1.50)#
+#
+dev.off()
+bsame <- 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+b <- sort(c(bsame, bdiff))#
+plot(density(bsame), col="blue", cex.lab=1.50, cex.axis=1.10, xlim=c(min(b), max(b)))#
+lines(density(bdiff), col="red")#
+rug(bsame, col="blue")#
+rug(bdiff, col="red")#
+sprintf("low = %.5f",b[1000])#
+sprintf("high = %.5f",b[1001])#
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+axis(3, at =1 ,labels='c = −3.2',cex.axis=1.50)
+setwd("/Users/suman/Documents/Postdoctoral_projects/Dec11_gist_simulations/Bayes_Factor_supp_Dec12/Bayes_Factor_suppli/simulations/gist/plotbf")
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+z90 = c(21,21,21,21,21,24,24,24,21,24,21,24,24,24,21,21,22,21,21,24,21,24,22,21,21,21,21,24,24,21,24,21,24,22,21,24,21,24,21,24,24,24,24,21,24,21,21,21,22,22,21,24,21,24,24,21,24,24,21,24,24,21,21,24,21,21,24,24,24,21,24,21,21,21,21,24,21,24,21,21,24,21,24,21,21,24,24,21,21,21,24,24,21,21,21,24,22,21,24,21,24,24,24,21,21,24,24,24,24,21,21,21,24,21,21,24,24,21,22,24,24,21,22,21,24,24,21,21,21,24,21,24,21,24,21,24,24,21,21,21,24,21,21,21,21,24,24,21,24,21,21,24,21,21,22,24,21,21,24,21,24,21,21,21,21,24,22,21,21,24,21,21,24,21,21,24,24,24,21,24,24,24,21,21,21,24,24,24,21,24,24,24,24,21,24,21,21,24,21,22,22,24,24,24,24,21,21,21,24,22,21,24,21,22,24,24,24,21,22,24,21,21,21,21,21,21,21,21,21,24,24,24,21,24,24,24,24,21,22,24,21,21,21,21,24,21,24,21,21,24,24,21,24,21,24,24,21,24,24,21,21,21,24,24,21,21,24,21,24,24,24,24,21,24,24,24,21,21,24,22,21,24,24,21,21,22,24,21,21,21,21,24,22,24,24,21,21,24,21,21,24,22,21,24,24,21,21,24,21,24,21,24,21,24,21,24,21,24,21,21,24,24,22,21,24,21,24,24,22,24,24,22,24,21,24,21,21,21,24,22,24,24,21,24,21,24,24,24,24,21,24,24,24,24,21,21,21,24,24,24,24,22,21,24,21,21,21,24,24,24,24,24,24,24,21,21,21,24,24,21,21,24,24,21,21,24,24,21,22,21,24,24,21,24,24,22,24,24,24,21,24,24,24,24,24,22,24,21,21,24,24,21,21,24,21,24,24,24,21,21,21,21,22,22,24,24,21,24,21,21,24,24,24,24,21,21,24,24,21,21,24,24,21,21,24,21,21,24,21,21,21,24,24,21,24,24,21,22,21,21,24,21,24,24,24,24,24,21,24,21,21,24,22,22,21,24,24,22,24,22,22,21,24,24,21,21,21,24,24,21,24,21,24,24,21,24,21,21,24,24,21,24,24,21,21,21,24,21,24,24,24,24,21,21,24,21,21,24,24,21,24,21,21,21,21,24,24,21,24,21,21,24,24,21,21,24,21,21,21,24,24,21,24,24,21,21,24,24,24,24,21,24,21,24,24,21,21,22,24,21,21,21,21,24,21,24,24,21,21,21,24,21,24,21,24,21,24,21,24,21,21,21,21,24,24,24,24,21,24,21,24,22,21,24,21,21,24,21,21,22,21,21,21,21,22,21,21,24,24,24,24,22,21,21,21,21,24,24,24,21,24,24,24,21,21,24,21,21,24,22,24,21,21,21,21,21,21,21,22,21,24,24,24,24,24,21,24,24,21,24,24,21,21,21,21,21,21,24,21,24,24,24,21,24,24,21,24,21,22,24,22,21,24,21,21,21,21,24,22,24,24,21,21,24,24,24,24,21,21,24,21,24,21,24,24,24,21,21,24,21,21,21,24,24,21,21,24,21,21,21,22,21,21,21,21,21,21,24,24,21,21,21,21,24,21,21,24,24,21,21,24,24,21,24,21,24,24,21,24,21,21,21,21,24,21,21,24,24,21,21,24,21,24,22,21,21,21,24,21,21,21,24,24,22,24,24,21,24,21,24,22,21,21,24,24,24,21,21,24,21,24,21,21,24,24,24,24,21,21,24,21,24,21,21,22,24,21,21,21,24,24,24,21,24,21,24,21,24,21,24,21,24,21,21,21,21,21,21,24,21,21,21,22,21,24,21,21,22,21,21,21,21,24,21,21,24,24,24,21,21,21,21,21,24,24,21,21,24,24,24,24,21,21,24,24,24,21,24,21,21,21,21,21,24,24,24,24,24,24,21,21,24,24,21,22,22,24,24,21,21,24,21,24,24,24,21,24,21,21,24,21,24,21,21,21,24,21,21,24,24,24,21,24,24,21,24,24,24,21,24,21,24,21,24,24,21,24,21,21,21,21,24,21,21,24,24,24,21,24,21,21,21,21,24,21,24,21,24,24,24,21,21,21,24,24,24,24,24,21,24,22,21,21,21,21,21,24,24,24,21,24,21,21,21,24,24,24,24,21,21,21,24,21,21,21,21,24,24,24,21,21,24,24,24,24,21,21,21,21,21,21,24,21,22,24,24,24,21,24,24)#
+c = 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+bgc = 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+pdf("a-Xdeep-Yd_new.pdf")#
+plot(x,y,type="p",pch=z90,col=c,bg=bgc,main="",xlab="Number of deep coalescences",ylab="D")#
+dev.off()
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.Rhistory b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.Rhistory
new file mode 100644
index 0000000..f05639e
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/.Rhistory
@@ -0,0 +1,512 @@
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topright",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+par(new=TRUE)
+qqnorm(BF)
+qqline(BF)
+require(MASS)
+library(MASS)
+ouput <- read.table("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/same/marg_likes_4_2_200reps.txt", header = T)
+BF <- ouput[,c(4)]
+BF
+fit <- fitdistr(BF, "normal")
+class(fit)
+para <- fit$estimate
+para
+set.seed(0); sim_data <- rnorm(200,-0.737250,1.142406)
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topright",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(10,10),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(10,10),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(12,12),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.5,
+cex = -0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = ')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 2,
+cex = 1.2,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+qqplot(x=BFt, y=sim_data, asp=1)
+qqplot(x=BF, y=sim_data, asp=1)
+abline( c(0,1))
+qqnorm(BF)
+qqline(BF)
+qqplot(x=sim-data, y=BF, asp=1)
+qqplot(x=sim_data, y=BF, asp=1)
+abline( c(0,1))
+qqplot(x=sim_data, y=BF, asp=1)
+abline( c(0,1))
+par(new=TRUE)
+qqnorm(BF)
+qqline(BF)
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+par(new=TRUE)
+qqnorm(BF)
+qqline(BF)
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+#par(new=TRUE)
+qqnorm(BF)
+qqline(BF)
+require(MASS)
+library(MASS)
+ouput <- read.table("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/same/marg_likes_4_2_1000reps.txt", header = T)
+BF <- ouput[,c(4)]
+require(MASS)
+library(MASS)
+ouput <- read.table("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/same/marg_likes_4_2_1000reps.txt", header = T)
+BF <- ouput[,c(4)]
+BF
+fit <- fitdistr(BF, "normal")
+class(fit)
+para <- fit$estimate
+para
+set.seed(0); sim_data <- rnorm(200,-0.6650600, 0.9690269 )
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 200')
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 1000')
+set.seed(0); sim_data <- rnorm(1000,-0.6650600, 0.9690269 )
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 1000')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+#qqplot(x=sim_data, y=BF, asp=1)
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+qqnorm(BF)
+qqline(BF)
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 1000')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+ouput <- read.table("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/same/marg_likes_6_2_200reps.txt", header = T)
+BF <- ouput[,c(4)]
+BF
+fit <- fitdistr(BF, "normal")
+class(fit)
+para <- fit$estimate
+para
+set.seed(0); sim_data <- rnorm(200,2.990000, 1.242694 )
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 1000')
+e <- density(sim_data)
+lines(e, col="red")
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 4, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 6, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+#qqplot(x=sim_data, y=BF, asp=1)
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+#par(new=TRUE)
+qqnorm(BF)
+qqline(BF)
+ouput <- read.table("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/same/marg_likes_6_2_1000reps.txt", header = T)
+BF <- ouput[,c(4)]
+BF
+fit <- fitdistr(BF, "normal")
+class(fit)
+para <- fit$estimate
+para
+set.seed(0); sim_data <- rnorm(1000,3.109580, 1.208208 )
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 6, rep = 1000')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+#qqplot(x=sim_data, y=BF, asp=1)
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+#par(new=TRUE)
+qqnorm(BF)
+qqline(BF)
+qqnorm(BF)
+qqline(BF)
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+ouput <- read.table("/Users/suman/Documents/Postdoctoral_projects/BF_calibration/same/marg_likes_6_10_200reps.txt", header = T)
+BF <- ouput[,c(4)]
+BF
+fit <- fitdistr(BF, "normal")
+class(fit)
+para <- fit$estimate
+para
+set.seed(0); sim_data <- rnorm(200,33.348250, 8.177051 )
+sim_data
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 6, num genes = 10, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topleft",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 6, num genes = 10, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topright",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+d <- density(BF)
+plot(d, col="blue", main= 'num taxa = 6, num genes = 10, rep = 200')
+e <- density(sim_data)
+lines(e, col="red")
+legend("topright",
+legend = c("BF", "simulated data"),
+col = c("blue", "red"),
+pch = c(19,19),
+bty = "n",
+pt.cex = 0.8,
+cex = 0.8,
+text.col = "black",
+horiz = F ,
+inset = c(0.1, 0.1))
+qqplot(x=sim_data, y=BF)
+abline( c(0,1))
+qqnorm(BF)
+qqline(BF)
+bsame <- c(0.5, 1.0, 3.0)
+bsame
+bsame <- c(0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+diff <- c(-3, -2, -1, 1.3, -12, -10, -86)
+b <- sort(c(bsame, bdiff))
+bdiff <- c(-3, -2, -1, 1.3, -12, -10, -86)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+bsame <- c(0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 0.1, -12, -10, -86)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+bsame <- c(0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 0.1, -12, -10, -86)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+bsame <- c(0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 0.13, -12, -10, -86)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+bsame <- c(0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 13, -12, -10, -86)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+1.0
+1.0
+1.0
+bsame <- c(-12,0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 13, -12, -10, -86, 4)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+bsame <- c(-15,0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 13, -12, -10, -86, 4)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[7])
+sprintf("high = %.5f",b[8])
+bsame <- c(-15,0.5, 1.0, 3.0, 4.0, 6.0, 7.0, 11.0)
+bdiff <- c(-3, -2, -1, 13, -12, -10, -86, 4)
+b <- sort(c(bsame, bdiff))
+b
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[8])
+sprintf("high = %.5f",b[9])
+setwd("/Users/suman/Documents/Postdoctoral_projects/Dec11_gist_simulations/Bayes_Factor_supp_Dec12/Bayes_Factor_suppli/simulations/gist/plotbf")
+output <- read.table("bfsummary.txt", header = T)
+BF = output$logc_topo_brlen-output$logc_unlinked
+sort(BF)
+bdiff <- c(-7888.49000,-5035.82000,-608.56000,-4415.34000,-190.89000,-5399.60000,-5105.41000,-3805.32000,-7603.95000,-10348.45000,-1285.91000,-11453.92000,-10150.52000,-6587.09000,-5920.91000,-3497.91000,-2537.61000,-8263.27000,-4191.50000,-11958.16000,-6356.07000,-1953.70000,-7078.03000,-364.17000,-5936.14000,-4812.01000,-1163.98000,-4299.52000,-6917.62000,-7714.09000,-5536.10000,-2797.59000,-3179.87000,-4064.89000,-9058.12000,-6273.83000,-9240.80000,-9964.82000,-4009.58000,-2696.01000,-10527.67000,-6014.06000,-6679.97000,-4944.72000,-4631.22000,-5236.34000,-8527.79000,-18.64000,-9366.37000,-5527.70000,-8770.73000,-8809.44000,-6318.71000,-6569.40000,-7788.38000,-5194.56000,-7771.95000,-6776.15000,-6766.98000,-5528.55000,-560.06000,-5224.21000,-4037.37000,-110.59000,-7493.94000,-4367.39000,-8871.25000,-5763.25000,-5687.39000,-1932.78000,-7118.74000,-3772.85000,-7142.56000,-7309.90000,-3963.96000,-11033.62000,-7056.65000,-4904.02000,-1704.39000,-3150.28000,-9476.08000,-1756.50000,-8635.39000,-8434.32000,-6735.28000,-5809.44000,-7072.53000,-418.71000,-4009.21000,-5297.32000,-10061.36000,-8187.72000,-7894.18000,-5222.31000,-2052.56000,-10117.69000,-4311.93000,-9769.55000,-1196.62000,-1082.46000,-3345.39000,-878.53000,-7348.28000,-12499.27000,-2710.46000,-10640.43000,-3843.85000,-4596.79000,-6069.02000,-4223.63000,-1756.53000,-5245.83000,-3257.32000,-4454.90000,-5555.09000,-5712.97000,-7206.33000,-9145.87000,-7936.21000,-6133.11000,-5468.06000,-3186.46000,-3302.43000,-2665.00000,-8045.25000,-7731.32000,-6514.42000,-5095.21000,-6432.50000,-7773.56000,-4128.10000,-3124.81000,-5397.69000,-3207.95000,-8652.82000,-3413.59000,-1913.93000,-9110.23000,-6411.20000,-2391.53000,-10094.69000,-9047.13000,-14007.06000,-5169.50000,-2434.52000,-8218.92000,-2852.73000,-6673.56000,-9749.94000,-5645.96000,-6716.57000,-7853.38000,-2179.67000,-4046.83000,-6690.11000,-792.02000,-6145.11000,-7143.08000,-7840.23000,-10006.97000,-6973.06000,-3289.15000,-2685.84000,-5899.03000,-8325.76000,-9513.94000,-6664.70000,-5882.65000,-2424.42000,-8924.41000,-7961.20000,-8153.67000,-2825.01000,-7714.04000,-3943.33000,-10443.04000,-2685.77000,-6362.95000,-2958.57000,-4496.63000,-7503.60000,-3318.37000,-6342.42000,-5390.70000,-813.71000,-2281.65000,-6365.13000,-2439.52000,-7747.74000,-1516.21000,-4216.77000,-8379.65000,-9391.51000,-6284.08000,-8053.57000,-3437.14000,-5737.36000,-5154.08000,-6933.08000,-3866.34000)
+sort(bdiff)
+bsame <- c(141.02000,164.21000,109.65000,90.47000,174.27000,123.28000,136.67000,172.98000,183.96000,185.14000,136.11000,167.46000,148.32000,193.86000,86.89000,142.14000,205.97000,81.62000,185.71000,151.38000,92.59000,154.78000,154.25000,-1.24000,133.88000,133.46000,168.46000,150.81000,166.80000,187.88000,181.17000,140.58000,170.54000,136.32000,145.89000,161.15000,132.40000,170.12000,125.79000,87.50000,146.06000,113.90000,173.41000,171.90000,167.41000,30.83000,118.09000,1.41000,122.38000,164.74000,114.40000,179.53000,145.35000,118.96000,117.26000,130.34000,133.81000,164.61000,157.99000,159.45000,142.79000,58.91000,137.62000,122.88000,203.51000,70.63000,135.08000,157.11000,146.16000,168.91000,117.02000,138.21000,85.54000,93.35000,83.86000,168.25000,153.19000,131.89000,152.03000,67.36000,123.86000,136.63000,146.33000,136.11000,140.86000,159.30000,161.77000,156.31000,186.63000,185.16000,109.38000,95.12000,126.72000,184.18000,189.85000,125.50000,146.38000,76.24000,158.26000,144.88000,185.19000,136.20000,144.67000,144.38000,124.63000,174.49000,127.72000,162.12000,136.39000,110.47000,179.73000,89.94000,113.68000,192.01000,177.80000,114.05000,164.16000,99.67000,114.00000,149.98000,139.59000,123.20000,132.14000,152.77000,153.30000,160.50000,117.26000,183.12000,170.89000,142.84000,152.61000,93.15000,183.58000,187.22000,82.15000,116.23000,159.86000,148.08000,116.01000,146.33000,108.67000,129.44000,122.93000,124.74000,175.75000,162.86000,163.98000,14.80000,5.68000,171.08000,77.97000,109.77000,183.82000,130.17000,153.02000,151.73000,131.95000,127.11000,159.25000,168.89000,186.22000,143.71000,102.53000,180.13000,162.87000,202.16000,139.36000,128.92000,-0.34000,210.94000,87.22000,162.57000,136.99000,153.95000,140.11000,159.08000,150.69000,123.28000,119.21000,124.57000,126.41000,162.42000,170.27000,140.75000,158.05000,134.58000,153.09000,142.46000,189.46000,176.11000,159.57000,103.65000,96.69000,155.87000,141.62000,155.80000,157.10000,148.39000,168.24000,145.56000,145.64000,122.78000,121.44000,194.13000,128.27000,159.57000,164.19000,185.06000,4.92000,102.25000,76.44000,134.98000,135.34000,141.81000,103.33000,173.66000,116.87000,138.77000,113.19000,160.13000,108.95000,171.85000,110.76000,173.23000,151.65000,143.73000,88.96000,140.58000,119.55000,180.24000,151.72000,189.24000,176.99000,186.80000,179.34000,106.92000,150.29000,160.09000,55.92000,174.63000,163.83000,139.57000,146.16000,118.95000,149.29000,184.05000,137.88000,134.25000,143.90000,156.49000,129.18000,163.12000,96.14000,196.43000,153.33000,126.08000,153.53000,159.23000,156.85000,1.33000,116.96000,169.67000,-2.85000,180.55000,167.94000,160.43000,53.94000,60.71000,156.64000,185.72000,65.09000,131.99000,155.94000,141.93000,96.39000,113.18000,80.00000,118.61000,107.21000,141.47000,147.43000,128.20000,74.90000,148.93000,183.11000,136.26000,111.27000,195.25000,125.62000,135.25000,185.22000,105.20000,195.05000,112.66000,103.53000,178.94000,150.83000,162.48000,163.79000,177.24000,93.20000,130.14000,20.54000,193.86000,143.35000,151.83000,172.20000,161.12000,173.94000,198.86000,144.35000,147.63000,134.38000,178.38000,2.30000,184.93000,142.56000,101.77000,137.03000,126.90000,139.97000,178.50000,156.66000,137.94000,196.08000,110.32000,148.23000,70.92000,120.64000,103.72000,62.41000,176.51000,106.44000,65.73000,188.48000,179.95000,155.64000,93.18000,186.12000,126.78000,179.22000,184.93000,17.24000,201.16000,113.11000,153.12000,159.50000,31.54000,116.60000,160.65000,135.60000,152.19000,160.14000,155.66000,143.25000,178.60000,107.73000,50.88000,134.28000,174.40000,144.64000,131.96000,141.11000,9.85000,143.13000,107.22000,0.60000,103.75000,122.12000,159.90000,173.26000,0.45000,106.46000,147.58000,70.10000,143.50000,117.19000,169.39000,161.22000,161.41000,158.25000,154.04000,85.03000,74.41000,135.40000,158.00000,138.93000,147.71000,168.40000,188.28000,160.27000,136.16000,212.39000,98.07000,164.29000,99.43000,115.88000,147.23000,136.94000,168.62000,159.92000,156.97000,183.47000,129.20000,141.90000,143.73000,164.89000,145.59000,132.58000,124.79000,142.03000,164.20000,179.50000,153.80000,27.18000,106.75000,155.48000,143.37000,131.89000,191.16000,129.74000,175.17000,135.78000,105.02000,78.03000,174.78000,140.77000,159.20000,107.93000,129.80000,156.36000,178.64000,150.04000,150.99000,195.44000,147.78000,134.27000,115.94000,162.30000,141.07000,67.80000,156.27000,150.35000,162.42000,82.33000,86.40000,118.00000,157.86000,95.94000,166.47000,168.66000,153.22000,84.31000,59.80000,139.07000,135.63000,185.25000,136.67000,192.42000,107.77000,155.98000,147.01000,211.02000,139.62000,161.08000,172.66000,180.76000,81.67000,125.99000,159.06000,176.45000,127.62000,144.91000,74.20000,110.89000,144.05000,185.77000,140.41000,183.17000,125.49000,179.79000,126.32000,129.14000,144.05000,164.84000,191.50000,15.21000,88.34000,120.40000,138.57000,127.26000,160.45000,92.54000,113.30000,105.91000,180.42000,16.91000,112.45000,99.22000,168.17000,162.73000,170.33000,169.75000,159.89000,173.28000,173.07000,132.46000,148.00000,164.83000,158.69000,156.14000,147.92000,107.47000,159.13000,136.21000,158.55000,171.20000,139.10000,145.09000,160.38000,119.90000,119.65000,25.86000,169.10000,173.53000,122.57000,184.62000,168.08000,169.72000,167.84000,120.17000,173.44000,124.92000,134.96000,169.81000,126.90000,141.31000,132.16000,134.30000,135.90000,140.48000,153.22000,171.95000,157.05000,135.52000,178.60000,121.53000,136.52000,136.51000,89.40000,181.77000,133.05000,139.14000,169.24000,141.72000,159.05000,128.63000,120.45000,137.65000,87.95000,183.51000,121.01000,131.81000,154.01000,156.10000,113.42000,102.68000,72.39000,166.61000,1.34000,156.26000,188.28000,165.58000,83.02000,159.19000,99.57000,122.61000,140.30000,154.36000,148.30000,142.68000,134.67000,135.05000,201.42000,99.16000,125.26000,156.33000,121.75000,111.04000,184.88000,64.19000,94.50000,143.99000,50.76000,105.34000,118.61000,139.00000,108.96000,134.38000,139.29000,158.43000,170.13000,150.93000,148.26000,105.95000,124.33000,113.63000,65.69000,161.65000,110.25000,171.95000,110.60000,146.52000,130.45000,165.69000,177.94000,138.51000,132.21000,130.08000,142.00000,201.22000,162.28000,161.38000,123.07000,189.41000,98.97000,135.64000,209.79000,109.48000,130.65000,84.80000,131.01000,161.95000,155.98000,144.52000,110.58000,170.16000,97.90000,164.89000,122.60000,179.68000,172.88000,113.27000,105.01000,94.51000,122.55000,149.37000,152.04000,176.55000,129.16000,137.98000,48.28000,185.03000,156.05000,163.62000,137.77000,163.27000,137.19000,116.15000,146.14000,135.08000,174.99000,145.37000,133.95000,157.03000,165.46000,168.56000,118.51000,141.00000,190.89000,124.71000,173.51000,136.96000,176.37000,115.50000,106.10000,174.07000,111.06000,156.32000,172.71000,84.30000,70.66000,175.86000,95.79000,132.70000,161.50000,130.35000,190.29000,128.86000,42.18000,163.35000,192.10000,139.66000,-1.86000,167.85000,22.41000,160.89000,158.46000,138.25000,174.58000,152.51000,182.21000,109.44000,172.94000,174.77000,80.81000,151.91000,146.93000,187.45000,73.54000,136.65000,142.86000,119.75000,11.74000,110.39000,36.67000,216.76000,98.38000,126.73000,106.72000,62.15000,103.55000,171.77000,120.29000,175.87000,138.31000,131.56000,168.85000,127.47000,163.03000,111.20000,127.31000,158.46000,163.76000,209.99000,147.58000,172.49000,185.08000,109.32000,142.60000,172.49000,175.98000,53.90000,169.57000,184.19000,116.19000,162.23000,139.19000,145.59000,116.65000,138.02000,157.24000,126.56000,190.89000,115.35000,69.25000,158.83000,128.60000,153.75000,141.78000,189.53000,196.28000,144.78000,84.47000,118.63000,180.56000,100.86000,184.08000,119.33000,191.56000,175.76000,160.47000,162.02000,120.08000,185.10000,117.38000,148.45000,49.76000,165.62000,116.20000,141.48000,135.04000,138.28000,119.19000,178.21000,126.23000,123.23000,84.07000,129.64000,77.07000,147.16000,131.20000,5.33000,178.95000,184.70000,169.41000,170.83000,63.91000,187.22000,121.75000,143.99000,121.04000,152.30000,171.18000,162.24000,115.32000,130.69000,82.36000,155.89000,134.08000,0.34000,136.89000,122.08000,181.58000,138.11000,178.09000,144.48000,148.80000,137.22000,170.35000,174.04000,111.69000,161.69000,198.88000,120.29000,152.57000,116.14000,145.43000,137.28000,114.88000,152.32000,146.57000,120.72000,160.51000,57.14000,151.37000,67.58000,109.48000,153.52000,152.16000,91.57000,182.18000,166.23000,154.40000,39.69000,107.81000,138.26000,164.50000,150.96000,153.54000,155.97000,58.91000,181.18000,167.97000,90.49000,147.15000,149.77000,42.65000,115.43000,109.93000,166.42000,181.27000,119.86000,159.24000,111.56000,151.13000,180.30000,106.27000,165.76000,143.76000,179.67000,177.72000,180.15000,135.36000,147.69000,50.42000,148.45000,183.38000,162.44000,159.97000,195.50000,160.55000,76.33000,118.41000,128.36000,33.51000,69.96000,154.01000,94.60000,190.80000,150.66000,27.92000,118.09000,154.42000,83.12000,58.82000,161.44000,151.19000,112.96000,158.32000,142.91000,147.16000,106.86000,106.67000,108.26000,175.64000,6.62000,136.09000,70.61000,114.30000,132.05000,121.50000,140.11000,167.50000,165.22000,87.69000,178.46000,87.97000,110.89000,177.53000,83.14000,180.68000,135.87000,150.22000,177.30000,124.14000,136.37000,122.80000,155.25000,149.29000,116.36000,57.14000,-1.44000,181.65000,152.70000,113.28000,175.69000,134.82000,86.62000,116.94000,97.34000,69.54000,53.73000,97.46000,130.18000,113.62000,173.25000,140.14000,136.47000,127.92000,126.31000,50.75000,60.27000,169.35000,140.66000,113.42000,138.24000,155.40000,143.10000,141.65000,127.93000,170.21000,83.59000,62.56000,143.91000,139.54000,174.27000,189.81000,134.93000,71.67000,147.11000,170.59000,112.24000,154.65000,44.87000,161.66000,139.52000,112.82000,156.38000,139.29000,175.11000,127.35000,175.82000,170.36000,166.54000,139.13000,144.63000,175.24000,155.79000,139.60000,186.85000,198.87000,102.85000,113.71000,135.60000,139.97000,113.20000,150.20000,151.39000,140.00000)
+bdiff <- c(-7888.49000,-5035.82000,-608.56000,-4415.34000,-190.89000,-5399.60000,-5105.41000,-3805.32000,-7603.95000,-10348.45000,-1285.91000,-11453.92000,-10150.52000,-6587.09000,-5920.91000,-3497.91000,-2537.61000,-8263.27000,-4191.50000,-11958.16000,-6356.07000,-1953.70000,-7078.03000,-364.17000,-5936.14000,-4812.01000,-1163.98000,-4299.52000,-6917.62000,-7714.09000,-5536.10000,-2797.59000,-3179.87000,-4064.89000,-9058.12000,-6273.83000,-9240.80000,-9964.82000,-4009.58000,-2696.01000,-10527.67000,-6014.06000,-6679.97000,-4944.72000,-4631.22000,-5236.34000,-8527.79000,-18.64000,-9366.37000,-5527.70000,-8770.73000,-8809.44000,-6318.71000,-6569.40000,-7788.38000,-5194.56000,-7771.95000,-6776.15000,-6766.98000,-5528.55000,-560.06000,-5224.21000,-4037.37000,-110.59000,-7493.94000,-4367.39000,-8871.25000,-5763.25000,-5687.39000,-1932.78000,-7118.74000,-3772.85000,-7142.56000,-7309.90000,-3963.96000,-11033.62000,-7056.65000,-4904.02000,-1704.39000,-3150.28000,-9476.08000,-1756.50000,-8635.39000,-8434.32000,-6735.28000,-5809.44000,-7072.53000,-418.71000,-4009.21000,-5297.32000,-10061.36000,-8187.72000,-7894.18000,-5222.31000,-2052.56000,-10117.69000,-4311.93000,-9769.55000,-1196.62000,-1082.46000,-3345.39000,-878.53000,-7348.28000,-12499.27000,-2710.46000,-10640.43000,-3843.85000,-4596.79000,-6069.02000,-4223.63000,-1756.53000,-5245.83000,-3257.32000,-4454.90000,-5555.09000,-5712.97000,-7206.33000,-9145.87000,-7936.21000,-6133.11000,-5468.06000,-3186.46000,-3302.43000,-2665.00000,-8045.25000,-7731.32000,-6514.42000,-5095.21000,-6432.50000,-7773.56000,-4128.10000,-3124.81000,-5397.69000,-3207.95000,-8652.82000,-3413.59000,-1913.93000,-9110.23000,-6411.20000,-2391.53000,-10094.69000,-9047.13000,-14007.06000,-5169.50000,-2434.52000,-8218.92000,-2852.73000,-6673.56000,-9749.94000,-5645.96000,-6716.57000,-7853.38000,-2179.67000,-4046.83000,-6690.11000,-792.02000,-6145.11000,-7143.08000,-7840.23000,-10006.97000,-6973.06000,-3289.15000,-2685.84000,-5899.03000,-8325.76000,-9513.94000,-6664.70000,-5882.65000,-2424.42000,-8924.41000,-7961.20000,-8153.67000,-2825.01000,-7714.04000,-3943.33000,-10443.04000,-2685.77000,-6362.95000,-2958.57000,-4496.63000,-7503.60000,-3318.37000,-6342.42000,-5390.70000,-813.71000,-2281.65000,-6365.13000,-2439.52000,-7747.74000,-1516.21000,-4216.77000,-8379.65000,-9391.51000,-6284.08000,-8053.57000,-3437.14000,-5737.36000,-5154.08000,-6933.08000,-3866.34000)
+b <- sort(c(bsame, bdiff))
+plot(density(bsame), col="blue", xlim=c(min(b), max(b)))
+lines(density(bdiff), col="red")
+rug(bsame, col="blue")
+rug(bdiff, col="red")
+sprintf("low = %.5f",b[600])
+sprintf("high = %.5f",b[601])
+bsame <- c(141.02000,164.21000,109.65000,90.47000,174.27000,123.28000,136.67000,172.98000,183.96000,185.14000,136.11000,167.46000,148.32000,193.86000,86.89000,142.14000,205.97000,81.62000,185.71000,151.38000,92.59000,154.78000,154.25000,-1.24000,133.88000,133.46000,168.46000,150.81000,166.80000,187.88000,181.17000,140.58000,170.54000,136.32000,145.89000,161.15000,132.40000,170.12000,125.79000,87.50000,146.06000,113.90000,173.41000,171.90000,167.41000,30.83000,118.09000,1.41000,122.38000,164.74000,114.40000,179.53000,145.35000,118.96000,117.26000,130.34000,133.81000,164.61000,157.99000,159.45000,142.79000,58.91000,137.62000,122.88000,203.51000,70.63000,135.08000,157.11000,146.16000,168.91000,117.02000,138.21000,85.54000,93.35000,83.86000,168.25000,153.19000,131.89000,152.03000,67.36000,123.86000,136.63000,146.33000,136.11000,140.86000,159.30000,161.77000,156.31000,186.63000,185.16000,109.38000,95.12000,126.72000,184.18000,189.85000,125.50000,146.38000,76.24000,158.26000,144.88000,185.19000,136.20000,144.67000,144.38000,124.63000,174.49000,127.72000,162.12000,136.39000,110.47000,179.73000,89.94000,113.68000,192.01000,177.80000,114.05000,164.16000,99.67000,114.00000,149.98000,139.59000,123.20000,132.14000,152.77000,153.30000,160.50000,117.26000,183.12000,170.89000,142.84000,152.61000,93.15000,183.58000,187.22000,82.15000,116.23000,159.86000,148.08000,116.01000,146.33000,108.67000,129.44000,122.93000,124.74000,175.75000,162.86000,163.98000,14.80000,5.68000,171.08000,77.97000,109.77000,183.82000,130.17000,153.02000,151.73000,131.95000,127.11000,159.25000,168.89000,186.22000,143.71000,102.53000,180.13000,162.87000,202.16000,139.36000,128.92000,-0.34000,210.94000,87.22000,162.57000,136.99000,153.95000,140.11000,159.08000,150.69000,123.28000,119.21000,124.57000,126.41000,162.42000,170.27000,140.75000,158.05000,134.58000,153.09000,142.46000,189.46000,176.11000,159.57000,103.65000,96.69000,155.87000,141.62000,155.80000,157.10000,148.39000,168.24000,145.56000,145.64000,122.78000,121.44000,194.13000,128.27000,159.57000,164.19000,185.06000,4.92000,102.25000,76.44000,134.98000,135.34000,141.81000,103.33000,173.66000,116.87000,138.77000,113.19000,160.13000,108.95000,171.85000,110.76000,173.23000,151.65000,143.73000,88.96000,140.58000,119.55000,180.24000,151.72000,189.24000,176.99000,186.80000,179.34000,106.92000,150.29000,160.09000,55.92000,174.63000,163.83000,139.57000,146.16000,118.95000,149.29000,184.05000,137.88000,134.25000,143.90000,156.49000,129.18000,163.12000,96.14000,196.43000,153.33000,126.08000,153.53000,159.23000,156.85000,1.33000,116.96000,169.67000,-2.85000,180.55000,167.94000,160.43000,53.94000,60.71000,156.64000,185.72000,65.09000,131.99000,155.94000,141.93000,96.39000,113.18000,80.00000,118.61000,107.21000,141.47000,147.43000,128.20000,74.90000,148.93000,183.11000,136.26000,111.27000,195.25000,125.62000,135.25000,185.22000,105.20000,195.05000,112.66000,103.53000,178.94000,150.83000,162.48000,163.79000,177.24000,93.20000,130.14000,20.54000,193.86000,143.35000,151.83000,172.20000,161.12000,173.94000,198.86000,144.35000,147.63000,134.38000,178.38000,2.30000,184.93000,142.56000,101.77000,137.03000,126.90000,139.97000,178.50000,156.66000,137.94000,196.08000,110.32000,148.23000,70.92000,120.64000,103.72000,62.41000,176.51000,106.44000,65.73000,188.48000,179.95000,155.64000,93.18000,186.12000,126.78000,179.22000,184.93000,17.24000,201.16000,113.11000,153.12000,159.50000,31.54000,116.60000,160.65000,135.60000,152.19000,160.14000,155.66000,143.25000,178.60000,107.73000,50.88000,134.28000,174.40000,144.64000,131.96000,141.11000,9.85000,143.13000,107.22000,0.60000,103.75000,122.12000,159.90000,173.26000,0.45000,106.46000,147.58000,70.10000,143.50000,117.19000,169.39000,161.22000,161.41000,158.25000,154.04000,85.03000,74.41000,135.40000,158.00000,138.93000,147.71000,168.40000,188.28000,160.27000,136.16000,212.39000,98.07000,164.29000,99.43000,115.88000,147.23000,136.94000,168.62000,159.92000,156.97000,183.47000,129.20000,141.90000,143.73000,164.89000,145.59000,132.58000,124.79000,142.03000,164.20000,179.50000,153.80000,27.18000,106.75000,155.48000,143.37000,131.89000,191.16000,129.74000,175.17000,135.78000,105.02000,78.03000,174.78000,140.77000,159.20000,107.93000,129.80000,156.36000,178.64000,150.04000,150.99000,195.44000,147.78000,134.27000,115.94000,162.30000,141.07000,67.80000,156.27000,150.35000,162.42000,82.33000,86.40000,118.00000,157.86000,95.94000,166.47000,168.66000,153.22000,84.31000,59.80000,139.07000,135.63000,185.25000,136.67000,192.42000,107.77000,155.98000,147.01000,211.02000,139.62000,161.08000,172.66000,180.76000,81.67000,125.99000,159.06000,176.45000,127.62000,144.91000,74.20000,110.89000,144.05000,185.77000,140.41000,183.17000,125.49000,179.79000,126.32000,129.14000,144.05000,164.84000,191.50000,15.21000,88.34000,120.40000,138.57000,127.26000,160.45000,92.54000,113.30000,105.91000,180.42000,16.91000,112.45000,99.22000,168.17000,162.73000,170.33000,169.75000,159.89000,173.28000,173.07000,132.46000,148.00000,164.83000,158.69000,156.14000,147.92000,107.47000,159.13000,136.21000,158.55000,171.20000,139.10000,145.09000,160.38000,119.90000,119.65000,25.86000,169.10000,173.53000,122.57000,184.62000,168.08000,169.72000,167.84000,120.17000,173.44000,124.92000,134.96000,169.81000,126.90000,141.31000,132.16000,134.30000,135.90000,140.48000,153.22000,171.95000,157.05000,135.52000,178.60000,121.53000,136.52000,136.51000,89.40000,181.77000,133.05000,139.14000,169.24000,141.72000,159.05000,128.63000,120.45000,137.65000,87.95000,183.51000,121.01000,131.81000,154.01000,156.10000,113.42000,102.68000,72.39000,166.61000,1.34000,156.26000,188.28000,165.58000,83.02000,159.19000,99.57000,122.61000,140.30000,154.36000,148.30000,142.68000,134.67000,135.05000,201.42000,99.16000,125.26000,156.33000,121.75000,111.04000,184.88000,64.19000,94.50000,143.99000,50.76000,105.34000,118.61000,139.00000,108.96000,134.38000,139.29000,158.43000,170.13000,150.93000,148.26000,105.95000,124.33000,113.63000,65.69000,161.65000,110.25000,171.95000,110.60000,146.52000,130.45000,165.69000,177.94000,138.51000,132.21000,130.08000,142.00000,201.22000,162.28000,161.38000,123.07000,189.41000,98.97000,135.64000,209.79000,109.48000,130.65000,84.80000,131.01000,161.95000,155.98000,144.52000,110.58000,170.16000,97.90000,164.89000,122.60000,179.68000,172.88000,113.27000,105.01000,94.51000,122.55000,149.37000,152.04000,176.55000,129.16000,137.98000,48.28000,185.03000,156.05000,163.62000,137.77000,163.27000,137.19000,116.15000,146.14000,135.08000,174.99000,145.37000,133.95000,157.03000,165.46000,168.56000,118.51000,141.00000,190.89000,124.71000,173.51000,136.96000,176.37000,115.50000,106.10000,174.07000,111.06000,156.32000,172.71000,84.30000,70.66000,175.86000,95.79000,132.70000,161.50000,130.35000,190.29000,128.86000,42.18000,163.35000,192.10000,139.66000,-1.86000,167.85000,22.41000,160.89000,158.46000,138.25000,174.58000,152.51000,182.21000,109.44000,172.94000,174.77000,80.81000,151.91000,146.93000,187.45000,73.54000,136.65000,142.86000,119.75000,11.74000,110.39000,36.67000,216.76000,98.38000,126.73000,106.72000,62.15000,103.55000,171.77000,120.29000,175.87000,138.31000,131.56000,168.85000,127.47000,163.03000,111.20000,127.31000,158.46000,163.76000,209.99000,147.58000,172.49000,185.08000,109.32000,142.60000,172.49000,175.98000,53.90000,169.57000,184.19000,116.19000,162.23000,139.19000,145.59000,116.65000,138.02000,157.24000,126.56000,190.89000,115.35000,69.25000,158.83000,128.60000,153.75000,141.78000,189.53000,196.28000,144.78000,84.47000,118.63000,180.56000,100.86000,184.08000,119.33000,191.56000,175.76000,160.47000,162.02000,120.08000,185.10000,117.38000,148.45000,49.76000,165.62000,116.20000,141.48000,135.04000,138.28000,119.19000,178.21000,126.23000,123.23000,84.07000,129.64000,77.07000,147.16000,131.20000,5.33000,178.95000,184.70000,169.41000,170.83000,63.91000,187.22000,121.75000,143.99000,121.04000,152.30000,171.18000,162.24000,115.32000,130.69000,82.36000,155.89000,134.08000,0.34000,136.89000,122.08000,181.58000,138.11000,178.09000,144.48000,148.80000,137.22000,170.35000,174.04000,111.69000,161.69000,198.88000,120.29000,152.57000,116.14000,145.43000,137.28000,114.88000,152.32000,146.57000,120.72000,160.51000,57.14000,151.37000,67.58000,109.48000,153.52000,152.16000,91.57000,182.18000,166.23000,154.40000,39.69000,107.81000,138.26000,164.50000,150.96000,153.54000,155.97000,58.91000,181.18000,167.97000,90.49000,147.15000,149.77000,42.65000,115.43000,109.93000,166.42000,181.27000,119.86000,159.24000,111.56000,151.13000,180.30000,106.27000,165.76000,143.76000,179.67000,177.72000,180.15000,135.36000,147.69000,50.42000,148.45000,183.38000,162.44000,159.97000,195.50000,160.55000,76.33000,118.41000,128.36000,33.51000,69.96000,154.01000,94.60000,190.80000,150.66000,27.92000,118.09000,154.42000,83.12000,58.82000,161.44000,151.19000,112.96000,158.32000,142.91000,147.16000,106.86000,106.67000,108.26000,175.64000,6.62000,136.09000,70.61000,114.30000,132.05000,121.50000,140.11000,167.50000,165.22000,87.69000,178.46000,87.97000,110.89000,177.53000,83.14000,180.68000,135.87000,150.22000,177.30000,124.14000,136.37000,122.80000,155.25000,149.29000,116.36000,57.14000,-1.44000,181.65000,152.70000,113.28000,175.69000,134.82000,86.62000,116.94000,97.34000,69.54000,53.73000,97.46000,130.18000,113.62000,173.25000,140.14000,136.47000,127.92000,126.31000,50.75000,60.27000,169.35000,140.66000,113.42000,138.24000,155.40000,143.10000,141.65000,127.93000,170.21000,83.59000,62.56000,143.91000,139.54000,174.27000,189.81000,134.93000,71.67000,147.11000,170.59000,112.24000,154.65000,44.87000,161.66000,139.52000,112.82000,156.38000,139.29000,175.11000,127.35000,175.82000,170.36000,166.54000,139.13000,144.63000,175.24000,155.79000,139.60000,186.85000,198.87000,102.85000,113.71000,135.60000,139.97000,113.20000,150.20000,151.39000,140.00000)
+b <- sort(c(bsame, bdiff))
+bsame <- c(141.02000,164.21000,109.65000,90.47000,174.27000,123.28000,136.67000,172.98000,183.96000,185.14000,136.11000,167.46000,148.32000,193.86000,86.89000,142.14000,205.97000,81.62000,185.71000,151.38000,92.59000,154.78000,154.25000,-1.24000,133.88000,133.46000,168.46000,150.81000,166.80000,187.88000,181.17000,140.58000,170.54000,136.32000,145.89000,161.15000,132.40000,170.12000,125.79000,87.50000,146.06000,113.90000,173.41000,171.90000,167.41000,30.83000,118.09000,1.41000,122.38000,164.74000,114.40000,179.53000,145.35000,118.96000,117.26000,130.34000,133.81000,164.61000,157.99000,159.45000,142.79000,58.91000,137.62000,122.88000,203.51000,70.63000,135.08000,157.11000,146.16000,168.91000,117.02000,138.21000,85.54000,93.35000,83.86000,168.25000,153.19000,131.89000,152.03000,67.36000,123.86000,136.63000,146.33000,136.11000,140.86000,159.30000,161.77000,156.31000,186.63000,185.16000,109.38000,95.12000,126.72000,184.18000,189.85000,125.50000,146.38000,76.24000,158.26000,144.88000,185.19000,136.20000,144.67000,144.38000,124.63000,174.49000,127.72000,162.12000,136.39000,110.47000,179.73000,89.94000,113.68000,192.01000,177.80000,114.05000,164.16000,99.67000,114.00000,149.98000,139.59000,123.20000,132.14000,152.77000,153.30000,160.50000,117.26000,183.12000,170.89000,142.84000,152.61000,93.15000,183.58000,187.22000,82.15000,116.23000,159.86000,148.08000,116.01000,146.33000,108.67000,129.44000,122.93000,124.74000,175.75000,162.86000,163.98000,14.80000,5.68000,171.08000,77.97000,109.77000,183.82000,130.17000,153.02000,151.73000,131.95000,127.11000,159.25000,168.89000,186.22000,143.71000,102.53000,180.13000,162.87000,202.16000,139.36000,128.92000,-0.34000,210.94000,87.22000,162.57000,136.99000,153.95000,140.11000,159.08000,150.69000,123.28000,119.21000,124.57000,126.41000,162.42000,170.27000,140.75000,158.05000,134.58000,153.09000,142.46000,189.46000,176.11000,159.57000,103.65000,96.69000,155.87000,141.62000,155.80000,157.10000,148.39000,168.24000,145.56000,145.64000,122.78000,121.44000,194.13000,128.27000,159.57000,164.19000,185.06000,4.92000,102.25000,76.44000,134.98000,135.34000,141.81000,103.33000,173.66000,116.87000,138.77000,113.19000,160.13000,108.95000,171.85000,110.76000,173.23000,151.65000,143.73000,88.96000,140.58000,119.55000,180.24000,151.72000,189.24000,176.99000,186.80000,179.34000,106.92000,150.29000,160.09000,55.92000,174.63000,163.83000,139.57000,146.16000,118.95000,149.29000,184.05000,137.88000,134.25000,143.90000,156.49000,129.18000,163.12000,96.14000,196.43000,153.33000,126.08000,153.53000,159.23000,156.85000,1.33000,116.96000,169.67000,-2.85000,180.55000,167.94000,160.43000,53.94000,60.71000,156.64000,185.72000,65.09000,131.99000,155.94000,141.93000,96.39000,113.18000,80.00000,118.61000,107.21000,141.47000,147.43000,128.20000,74.90000,148.93000,183.11000,136.26000,111.27000,195.25000,125.62000,135.25000,185.22000,105.20000,195.05000,112.66000,103.53000,178.94000,150.83000,162.48000,163.79000,177.24000,93.20000,130.14000,20.54000,193.86000,143.35000,151.83000,172.20000,161.12000,173.94000,198.86000,144.35000,147.63000,134.38000,178.38000,2.30000,184.93000,142.56000,101.77000,137.03000,126.90000,139.97000,178.50000,156.66000,137.94000,196.08000,110.32000,148.23000,70.92000,120.64000,103.72000,62.41000,176.51000,106.44000,65.73000,188.48000,179.95000,155.64000,93.18000,186.12000,126.78000,179.22000,184.93000,17.24000,201.16000,113.11000,153.12000,159.50000,31.54000,116.60000,160.65000,135.60000,152.19000,160.14000,155.66000,143.25000,178.60000,107.73000,50.88000,134.28000,174.40000,144.64000,131.96000,141.11000,9.85000,143.13000,107.22000,0.60000,103.75000,122.12000,159.90000,173.26000,0.45000,106.46000,147.58000,70.10000,143.50000,117.19000,169.39000,161.22000,161.41000,158.25000,154.04000,85.03000,74.41000,135.40000,158.00000,138.93000,147.71000,168.40000,188.28000,160.27000,136.16000,212.39000,98.07000,164.29000,99.43000,115.88000,147.23000,136.94000,168.62000,159.92000,156.97000,183.47000,129.20000,141.90000,143.73000,164.89000,145.59000,132.58000,124.79000,142.03000,164.20000,179.50000,153.80000,27.18000,106.75000,155.48000,143.37000,131.89000,191.16000,129.74000,175.17000,135.78000,105.02000,78.03000,174.78000,140.77000,159.20000,107.93000,129.80000,156.36000,178.64000,150.04000,150.99000,195.44000,147.78000,134.27000,115.94000,162.30000,141.07000,67.80000,156.27000,150.35000,162.42000,82.33000,86.40000,118.00000,157.86000,95.94000,166.47000,168.66000,153.22000,84.31000,59.80000,139.07000,135.63000,185.25000,136.67000,192.42000,107.77000,155.98000,147.01000,211.02000,139.62000,161.08000,172.66000,180.76000,81.67000,125.99000,159.06000,176.45000,127.62000,144.91000,74.20000,110.89000,144.05000,185.77000,140.41000,183.17000,125.49000,179.79000,126.32000,129.14000,144.05000,164.84000,191.50000,15.21000,88.34000,120.40000,138.57000,127.26000,160.45000,92.54000,113.30000,105.91000,180.42000,16.91000,112.45000,99.22000,168.17000,162.73000,170.33000,169.75000,159.89000,173.28000,173.07000,132.46000,148.0000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+b <- sort(c(bsame, bdiff))
+bdiff <- c(-7888.49000,-5035.82000,-608.56000,-4415.34000,-190.89000,-5399.60000,-5105.41000,-3805.32000,-7603.95000,-10348.45000,-1285.91000,-11453.92000,-10150.52000,-6587.09000,-5920.91000,-3497.91000,-2537.61000,-8263.27000,-4191.50000,-11958.16000,-6356.07000,-1953.70000,-7078.03000,-364.17000,-5936.14000,-4812.01000,-1163.98000,-4299.52000,-6917.62000,-7714.09000,-5536.10000,-2797.59000,-3179.87000,-4064.89000,-9058.12000,-6273.83000,-9240.80000,-9964.82000,-4009.58000,-2696.01000,-10527.67000,-6014.06000,-6679.97000,-4944.72000,-4631.22000,-5236.34000,-8527.79000,-18.64000,-9366.37000,-5527.70000,-8770.73000,-8809.44000,-6318.71000,-6569.40000,-7788.38000,-5194.56000,-7771.95000,-6776.15000,-6766.98000,-5528.55000,-560.06000,-5224.21000,-4037.37000,-110.59000,-7493.94000,-4367.39000,-8871.25000,-5763.25000,-5687.39000,-1932.78000,-7118.74000,-3772.85000,-7142.56000,-7309.90000,-3963.96000,-11033.62000,-7056.65000,-4904.02000,-1704.39000,-3150.28000,-9476.08000,-1756.50000,-8635.39000,-8434.32000,-6735.28000,-5809.44000,-7072.53000,-418.71000,-4009.21000,-5297.32000,-10061.36000,-8187.72000,-7894.18000,-5222.31000,-2052.56000,-10117.69000,-4311.93000,-9769.55000,-1196.62000,-1082.46000,-3345.39000,-878.53000,-7348.28000,-12499.27000,-2710.46000,-10640.43000,-3843.85000,-4596.79000,-6069.02000,-4223.63000,-1756.53000,-5245.83000,-3257.32000,-4454.90000,-5555.09000,-5712.97000,-7206.33000,-9145.87000,-7936.21000,-6133.11000,-5468.06000,-3186.46000,-3302.43000,-2665.00000,-8045.25000,-7731.32000,-6514.42000,-5095.21000,-6432.50000,-7773.56000,-4128.10000,-3124.81000,-5397.69000,-3207.95000,-8652.82000,-3413.59000,-1913.93000,-9110.23000,-6411.20000,-2391.53000,-10094.69000,-9047.13000,-14007.06000,-5169.50000,-2434.52000,-8218.92000,-2852.73000,-6673.56000,-9749.94000,-5645.96000,-6716.57000,-7853.38000,-2179.67000,-4046.83000,-6690.11000,-792.02000,-6145.11000,-7143.08000,-7840.23000,-10006.97000,-6973.06000,-3289.15000,-2685.84000,-5899.03000,-8325.76000,-9513.94000,-6664.70000,-5882.65000,-2424.42000,-8924.41000,-7961.20000,-8153.67000,-2825.01000,-7714.04000,-3943.33000,-10443.04000,-2685.77000,-6362.95000,-2958.57000,-4496.63000,-7503.60000,-3318.37000,-6342.42000,-5390.70000,-813.71000,-2281.65000,-6365.13000,-2439.52000,-7747.74000,-1516.21000,-4216.77000,-8379.65000,-9391.51000,-6284.08000,-8053.57000,-3437.14000,-5737.36000,-5154.08000,-6933.08000,-3866.34000)
+b <- sort(c(bsame, bdiff))
+bsame <- 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diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/1_1000_bfsummary.txt b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/1_1000_bfsummary.txt
new file mode 100644
index 0000000..23bd981
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/1_1000_bfsummary.txt
@@ -0,0 +1,2001 @@
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diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a-Xdeep-Yd.pdf b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a-Xdeep-Yd.pdf
new file mode 100644
index 0000000..f77cda3
Binary files /dev/null and b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a-Xdeep-Yd.pdf differ
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a.R b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a.R
new file mode 100644
index 0000000..7a53120
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a.R
@@ -0,0 +1,10 @@
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+c = 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+bgc = 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+pdf("a-Xdeep-Yd.pdf")
+plot(x,y,type="p",pch=z90,col=c,bg=bgc,main="",xlab="Number of deep coalescences",ylab="D")
+dev.off()
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a.html b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a.html
new file mode 100644
index 0000000..b9e9061
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/a.html
@@ -0,0 +1,490 @@
+
+
+
+
+ Deep coalescence, Bayes factors, phylogenetic information, and phylogenetic dissonance
+
+
+
+
+
+
+
+ Show replicate numbers
+ Y-axis?
+
+
+
+
+
+
+
+
+
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/b.R b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/b.R
new file mode 100644
index 0000000..5e36956
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/b.R
@@ -0,0 +1,10 @@
+library(rgb)
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+z90 = c(21,24,24,24,24,22,21,24,22,24,21,21,24,21,24,21,24,24,24,24,21,24,21,21,21,22,24,24,24,24,21,24,24,24,21,21,21,21,21,21,21,24,24,21,21,21,24,24,21,21,21,24,24,21,21,21,24,21,21,21,24,24,24,21,21,24,24,21,21,21,24,21,21,24,21,21,24,21,21,21,24,21,21,21,22,21,21,24,24,24,24,21,24,21,24,24,24,21,22,21,21,21,24,21,24,24,22,21,21,24,22,24,21,21,24,21,21,21,24,21,24,24,24,24,21,21,24,21,24,21,21,21,24,24,21,24,24,21,21,21,21,22,21,24,24,24,21,24,21,21,21,24,24,24,21,24,21,24,21,21,21,24,21,22,21,24,24,21,24,22,24,21,24,24,24,21,21,24,21,24,24,21,22,21,24,22,24,24,22,24,21,24,24,22,21,21,24,21,24,21,21,24,21,24,22,24,24,21,24,24,21,21,21,21,21,21,24,21,24,24,21,24,24,21,21,22,24,24,21,21,24,21,24,24,21,21,24,21,24,24,21,21,21,21,22,24,22,21,24,21,24,24,24,24,21,24,22,24,21,21,21,21,24,21,24,24,21,24,21,24,21,21,24,24,21,22,21,24,21,21,24,21,21,24,21,21,24,21,24,21,21,21,21,24,21,24,21,21,24,21,24,21,24,24,21,21,24,21,21,24,24,24,24,21,24,24,21,21,22,24,24,24,24,21,21,24,24,24,24,21,21,21,24,21,22,24,21,24,24,21,24,21,21,21,21,21,24,24,24,24,21,24,24,21,24,21,24,24,21,24,21,21,21,21,21,21,21,24,21,24,24,21,24,21,24,21,24,24,24,21,21,24,24,24,21,21,21,24,24,21,21,24,24,24,24,21,21,24,21,24,21,21,21,24,24,21,21,21,24,21,22,24,21,22,21,21,21,24,22,21,24,21,24,24,21,24,21,22,22,21,21,21,24,22,21,24,24,21,24,24,24,24,21,24,24,24,22,24,21,21,22,21,21,21,21,24,24,21,24,22,24,21,24,24,21,24,22,22,24,24,24,21,24,21,24,24,24,24,24,22,24,24,24,22,24,21,21,22,21,21,24,21,24,21,21,24,21,21,21,21,21,21,21,24,24,21,21,21,21,21,21,24,22,21,21,21,24,24,21,24,21,21,21,24,24,21,24,21,24,21,21,24,21,24,21,24,21,24,21,21,21,21,22,21,24,21,21,21,21,24,24,21,21,21,22,24,24,24,21,24,21,24,22,21,21,24,21,24,21,24,24,21,21,24,24,24,24,24,21,24,24,21,21,21,21,21,21,21,22,21,21,22,24,21,21,21,24,22,24,21,24,24,24,24,24,24,24,21,21,21,24,24,21,24,21,21,24,21,21,24,24,24,24,21,21,21,24,24,22,24,24,21,24,24,24,21,24,24,21,24,21,21,21,21,24,21,24,24,24,24,21,21,21,21,21,21,21,21,21,21,21,21,21,24,21,24,21,21,24,24,24,22,24,21,24,21,24,24,24,21,21,21,24,21,21,21,24,21,24,21,21,22,21,24,21,24,24,24,24,24,21,24,24,21,21,21,21,21,24,24,21,24,21,21,24,21,24,24,24,24,24,21,24,21,24,21,21,24,24,24,21,24,21,24,21,21,21,21,21,24,24,21,24,21,21,21,21,24,24,24,24,21,21,24,24,21,24,24,21,21,21,21,21,21,21,24,21,21,24,24,24,24,21,21,24,24,24,24,21,21,21,21,21,21,24,21,24,24,21,22,24,21,21,21,24,24,24,24,24,24,24,24,21,24,22,24,21,21,21,24,21,21,24,24,24,24,21,24,21,21,24,24,21,21,21,24,21,24,21,21,24,21,21,21,21,24,24,24,21,24,21,24,22,24,21,24,24,21,21,24,21,24,21,24,24,24,24,24,21,24,21,21,24,21,24,24,21,21,24,21,24,24,24,21,21,21,21,21,21,21,21,21,24,21,21,21,21,21,21,21,21,21,21,24,24,24,21,24,24,24,21,21,24,21,21,21,24,24,24,21,21,24,21,24,21,21,24,24,24,21,21,21,21,24,24,24,24,24,21,21,24,24,24,21,24,24,21,21,21,22,21,21,21,24,24,24,24,21,21,24,21,24,24,21,24,24,21,21,22,22,24,21,24,24,21,21,21,21,24,24,21,24,24,21,21,22,22,21,24,21,24,22,21,22,21,24,24,21,21,24,24,21,21,21,24,21,24,21,24,21)
+c = c("blue","red","red","red","red","green","blue","red","green","red","blue","blue","red","blue","red","blue","red","red","red","red","blue","red","blue","blue","blue","green","red","red","red","red","blue","red","red","red","blue","blue","blue","blue","blue","blue","blue","red","red","blue","blue","blue","red","red","blue","blue","blue","red","red","blue","blue","blue","red","blue","blue","blue","red","red","red","blue","blue","red","red","blue","blue","blue","red","blue","blue","red","blue","blue","red","blue","blue","blue","red","blue","blue","blue","green","blue","blue","red","red","red","red","blue","red","blue","red","red","red","blue","green","blue","blue","blue","red","blue","red","red","green","blue","blue","red","green","red","blue","blue","red","blue","blue","blue","red","blue","red","red","red","red","blue","blue","red","blue","red","blue","blue","blue","red","red","blue","red","red","blue","blue","blue","blue","green","blue","red","red","red","blue","red","blue","blue","blue","red","red","red","blue","red","blue","red","blue","blue","blue","red","blue","green","blue","red","red","blue","red","green","red","blue","red","red","red","blue","blue","red","blue","red","red","blue","green","blue","red","green","red","red","green","red","blue","red","red","green","blue","blue","red","blue","red","blue","blue","red","blue","red","green","red","red","blue","red","red","blue","blue","blue","blue","blue","blue","red","blue","red","red","blue","red","red","blue","blue","green","red","red","blue","blue","red","blue","red","red","blue","blue","red","blue","red","red","blue","blue","blue","blue","green","red","green","blue","red","blue","red","red","red","red","blue","red","green","red","blue","blue","blue","blue","red","blue","red","red","blue","red","blue","red","blue","blue","red","red","blue","green","blue","red","blue","blue","red","blue","blue","red","blue","blue","red","blue","red","blue","blue","blue","blue","red","blue","red","blue","blue","red","blue","red","blue","red","red","blue","blue","red","blue","blue","red","red","red","red","blue","red","red","blue","blue","green","red","red","red","red","blue","blue","red","red","red","red","blue","blue","blue","red","blue","green","red","blue","red","red","blue","red","blue","blue","blue","blue","blue","red","red","red","red","blue","red","red","blue","red","blue","red","red","blue","red","blue","blue","blue","blue","blue","blue","blue","red","blue","red","red","blue","red","blue","red","blue","red","red","red","blue","blue","red","red","red","blue","blue","blue","red","red","blue","blue","red","red","red","red","blue","blue","red","blue","red","blue","blue","blue","red","red","blue","blue","blue","red","blue","green","red","blue","green","blue","blue","blue","red","green","blue","red","blue","red","red","blue","red","blue","green","green","blue","blue","blue","red","green","blue","red","red","blue","red","red","red","red","blue","red","red","red","green","red","blue","blue","green","blue","blue","blue","blue","red","red","blue","red","green","red","blue","red","red","blue","red","green","green","red","red","red","blue","red","blue","red","red","red","red","red","green","red","red","red","green","red","blue","blue","green","blue","blue","red","blue","red","blue","blue","red","blue","blue","blue","blue","blue","blue","blue","red","red","blue","blue","blue","blue","blue","blue","red","green","blue","blue","blue","red","red","blue","red","blue","blue","blue","red","red","blue","red","blue","red","blue","blue","red","blue","red","blue","red","blue","red","blue","blue","blue","blue","green","blue","red","blue","blue","blue","blue","red","red","blue","blue","blue","green","red","red","red","blue","red","blue","red","green","blue","blue","red","blue","red","blue","red","red","blue","blue","red","red","red","red","red","blue","red","red","blue","blue","blue","blue","blue","blue","blue","green","blue","blue","green","red","blue","blue","blue","red","green","red","blue","red","red","red","red","red","red","red","blue","blue","blue","red","red","blue","red","blue","blue","red","blue","blue","red","red","red","red","blue","blue","blue","red","red","green","red","red","blue","red","red","red","blue","red","red","blue","red","blue","blue","blue","blue","red","blue","red","red","red","red","blue","blue","blue","blue","blue","blue","blue","blue","blue","blue","blue","blue","blue","red","blue","red","blue","blue","red","red","red","green","red","blue","red","blue","red","red","red","blue","blue","blue","red","blue","blue","blue","red","blue","red","blue","blue","green","blue","red","blue","red","red","red","red","red","blue","red","red","blue","blue","blue","blue","blue","red","red","blue","red","blue","blue","red","blue","red","red","red","red","red","blue","red","blue","red","blue","blue","red","red","red","blue","red","blue","red","blue","blue","blue","blue","blue","red","red","blue","red","blue","blue","blue","blue","red","red","red","red","blue","blue","red","red","blue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+bgc = c("white","white","white","white","white","green","blue","red","green","white","white","white","red","white","white","white","white","red","red","white","white","white","blue","white","white","white","white","red","red","white","blue","white","white","white","white","white","white","white","white","white","white","red","white","white","white","white","red","red","blue","blue","white","white","red","white","white","white","white","blue","blue","white","white","white","red","white","blue","white","white","blue","blue","white","red","white","white","red","white","blue","white","white","white","white","white","blue","blue","white","green","white","white","red","white","red","red","blue","white","blue","white","red","white","white","green","white","white","white","white","white","white","white","green","white","white","white","green","white","white","blue","white","white","blue","white","red","white","red","red","white","white","white","white","red","blue","red","white","white","white","red","white","white","white","red","white","white","white","white","white","white","red","white","white","white","red","white","white","blue","white","white","red","white","white","white","white","white","white","white","red","white","green","white","red","white","blue","red","green","white","white","red","white","red","white","white","red","white","white","white","white","green","blue","red","green","white","red","green","red","white","white","red","white","blue","white","red","white","red","white","white","red","white","white","green","white","white","white","red","white","white","white","white","blue","white","white","white","blue","red","white","blue","red","white","white","blue","green","red","white","blue","blue","red","white","red","red","white","white","white","white","red","red","blue","white","white","blue","green","red","green","white","red","white","white","red","red","red","white","white","green","white","blue","white","white","white","red","blue","white","red","blue","red","white","white","white","white","red","white","white","green","white","white","white","white","white","white","white","white","blue","white","red","blue","red","white","white","white","white","red","white","red","blue","blue","white","blue","white","white","white","red","white","white","white","blue","white","red","white","white","red","white","white","red","white","white","green","white","white","white","white","blue","white","white","white","red","white","blue","white","white","red","white","green","white","white","white","white","white","red","blue","blue","blue","white","white","white","red","red","red","blue","red","white","blue","white","blue","red","white","white","white","blue","white","white","white","white","white","blue","red","blue","red","red","white","red","blue","red","blue","red","red","red","blue","white","white","red","white","white","white","white","red","red","white","white","white","white","white","red","blue","blue","white","white","red","white","white","white","white","white","blue","blue","white","red","blue","green","white","white","green","blue","blue","blue","white","white","white","red","white","white","white","white","red","white","green","white","white","white","white","red","green","blue","white","red","white","white","red","white","red","white","red","white","white","green","red","white","white","green","white","blue","blue","white","red","white","blue","red","green","white","white","white","white","blue","white","green","green","red","white","white","white","white","white","red","white","white","white","white","green","white","white","red","green","white","white","white","green","white","blue","red","white","white","white","white","red","white","blue","white","white","white","white","white","white","red","blue","blue","white","blue","blue","white","red","green","blue","blue","white","red","red","white","white","blue","white","white","red","red","white","white","white","white","white","blue","white","white","red","blue","white","white","white","blue","blue","blue","blue","green","blue","red","blue","white","white","white","white","white","white","white","blue","green","red","red","red","white","white","white","red","green","white","white","red","white","red","white","red","red","white","white","red","white","red","white","red","blue","white","red","blue","white","blue","blue","blue","blue","white","green","white","white","white","red","white","white","white","white","white","white","blue","white","red","red","red","white","white","red","white","white","white","red","white","white","red","white","blue","red","white","white","white","white","white","red","white","white","white","white","white","green","white","white","white","red","red","red","white","white","red","blue","red","white","white","blue","white","red","white","red","white","red","red","white","white","blue","blue","blue","white","white","white","white","blue","blue","white","blue","white","white","red","white","white","red","white","red","green","white","b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+pdf("b-Xdeep-Yd.pdf")
+plot(x,y,type="p",pch=z90,col=c,bg=bgc,main="",xlab="Number of deep coalescences",ylab="D")
+dev.off()
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/b.html b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/b.html
new file mode 100644
index 0000000..1751e97
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/b.html
@@ -0,0 +1,490 @@
+
+
+
+
+ Deep coalescence, Bayes factors, phylogenetic information, and phylogenetic dissonance
+
+
+
+
+
+
+
+ Show replicate numbers
+ Y-axis?
+
+
+
+
+
+
+
+
+
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/doofus.svg b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/doofus.svg
new file mode 100644
index 0000000..6c3afa6
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/doofus.svg
@@ -0,0 +1 @@
+
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/plot-template.html b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/plot-template.html
new file mode 100755
index 0000000..e175e2c
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/plot-template.html
@@ -0,0 +1,488 @@
+
+
+
+
+ Deep coalescence, Bayes factors, phylogenetic information, and phylogenetic dissonance
+
+
+
+
+
+
+
+ Show replicate numbers
+ Y-axis?
+
+
+
+
+
+
+
+
+
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/plot_bayes_factors.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/plot_bayes_factors.py
new file mode 100644
index 0000000..24cb921
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/plotbf/plot_bayes_factors.py
@@ -0,0 +1,283 @@
+import sys,re,math
+
+annotate_plot = False
+
+def readBFSummary(fpath):
+ # dirname theta bigT sheight gshortest gtallest logc_unlinked logc_topo logc_topo_brlen Cmerged Iaverage d deep Unlinked Tlinked TBlinked
+ # run5b 0.18584 0.30406 0.38351 0.48904 0.49359 -20088.43000 -20085.27000 -20151.53000 1.00000 100.00000 0.00000 2 0 1 0
+
+ a = [] # holds results from run1a, run2a, etc.
+ b = [] # holds results from run1b, run2b, etc.
+ lines = open(fpath, 'r').readlines()
+ print '%d lines in %s' % (len(lines), fpath)
+ headers = lines[0].strip().split()
+ assert len(headers) == 20, 'Expecting 20 columns in %s, but found %d for line 1' % (fpath, len(headers))
+ for i,line in enumerate(lines[1:]):
+ parts = line.strip().split()
+ assert len(parts) == 20, 'Expecting 20 columns in %s, but found %d for line %d' % (fpath,len(parts),i+2)
+ dirname = parts[0]
+ nconflicting = parts[1]
+ nconstant = parts[2]
+ nvariable = parts[3]
+ nparsimony = parts[4]
+ theta = parts[5]
+ bigT = parts[6]
+ sheight = parts[7]
+ gshortest = parts[8]
+ gtallest = parts[9]
+ logc_unlinked = parts[10]
+ logc_topo = parts[11]
+ logc_topo_brlen = parts[12]
+ Cmerged = parts[13]
+ Iaverage = parts[14]
+ d = parts[15]
+ deep = parts[16]
+ unlinked = parts[17]
+ tlinked = parts[18]
+ tblinked = parts[19]
+
+ # add to the appropriate dictionary, a or b, depending on the last letter in dirname
+ m = re.match('run(\d+)(a|b)', dirname)
+ assert m is not None
+ number = int(m.group(1))
+ which = m.group(2)
+ if which == 'a':
+ a.append({
+ 'index':number,
+ 'dirname':dirname,
+ 'nconflicting':nconflicting,
+ 'nconstant':nconstant,
+ 'nvariable':nvariable,
+ 'nparsimony':nparsimony,
+ 'theta':theta,
+ 'bigT':bigT,
+ 'sheight':sheight,
+ 'gshortest':gshortest,
+ 'gtallest':gtallest,
+ 'deep':deep,
+ 'logc':logc_unlinked,
+ 'logc_topo':logc_topo,
+ 'logc_topo_brlen':logc_topo_brlen,
+ 'Cmerged':Cmerged,
+ 'Iaverage':Iaverage,
+ 'D':d,
+ 'deep':deep,
+ 'unlinked':unlinked,
+ 'tlinked':tlinked,
+ 'tblinked':tblinked
+ })
+ else:
+ b.append({
+ 'index':number,
+ 'dirname':dirname,
+ 'nconflicting':nconflicting,
+ 'nconstant':nconstant,
+ 'nvariable':nvariable,
+ 'nparsimony':nparsimony,
+ 'theta':theta,
+ 'bigT':bigT,
+ 'sheight':sheight,
+ 'gshortest':gshortest,
+ 'gtallest':gtallest,
+ 'deep':deep,
+ 'logc':logc_unlinked,
+ 'logc_topo':logc_topo,
+ 'logc_topo_brlen':logc_topo_brlen,
+ 'Cmerged':Cmerged,
+ 'Iaverage':Iaverage,
+ 'D':d,
+ 'deep':deep,
+ 'unlinked':unlinked,
+ 'tlinked':tlinked,
+ 'tblinked':tblinked
+ })
+ return a, b
+
+def plotRun(which, data, calibration):
+ deep = []
+ d = []
+ z90 = []
+ color = []
+ bgcolor = []
+ labels = []
+
+ #voronoi_sites = []
+ d3dataset = []
+ maxdissonance = 0.0;
+ maxdeep = 0.0;
+
+ for rep in data:
+
+ replicate = int(rep['index'])
+ labels.append(rep['index'])
+ iaverage = float(rep['Iaverage'])
+ nconflicting = int(rep['nconflicting'])
+ nconstant = int(rep['nconstant'])
+ nvariable = int(rep['nvariable'])
+ nparsimony = int(rep['nparsimony'])
+ theta = float(rep['theta'])
+ bigT = float(rep['bigT'])
+ theta_over_bigT = theta/bigT
+ sheight = float(rep['sheight'])
+ gshortest = float(rep['gshortest'])
+ gtallest = float(rep['gtallest'])
+ winner = 'gray'
+ if calibration is True:
+ T = float(rep['logc_topo'])
+ TB = float(rep['logc_topo_brlen'])
+ U = float(rep['logc'])
+ if (TB-U>0.0) and (TB-T>0.0):
+ color.append('blue')
+ z90.append(21) # circle
+ if iaverage > 90.0:
+ bgcolor.append('blue')
+ else:
+ bgcolor.append('white')
+ winner = 'blue'
+
+# elif U > T and U > TB:
+ elif (0.0>=TB-U >=-3.2):
+ #print TB-U
+ color.append('blue')
+ z90.append(24) # triangle (point up)
+ if iaverage > 90.0:
+ bgcolor.append('blue')
+ else:
+ bgcolor.append('white')
+ winner = 'blue'
+
+ elif T > TB and T > U:
+ color.append('green')
+ z90.append(22) # square
+ if iaverage > 90.0:
+ bgcolor.append('green')
+ else:
+ bgcolor.append('white')
+ winner = 'green'
+
+# elif U > T and U > TB:
+ elif (TB-U < -3.2):
+ color.append('red')
+ z90.append(24) # triangle (point up)
+ if iaverage > 90.0:
+ bgcolor.append('red')
+ else:
+ bgcolor.append('white')
+ winner = 'red'
+ else:
+ assert(False), 'Should not have come here'
+ else:
+ T = float(rep['logc_topo'])
+ TB = float(rep['logc_topo_brlen'])
+ U = float(rep['logc'])
+ if TB > T and TB > U:
+ color.append('blue')
+ z90.append(21) # circle
+ if iaverage > 90.0:
+ bgcolor.append('blue')
+ else:
+ bgcolor.append('white')
+ winner = 'blue'
+ elif T > TB and T > U:
+ color.append('green')
+ z90.append(22) # square
+ if iaverage > 90.0:
+ bgcolor.append('green')
+ else:
+ bgcolor.append('white')
+ winner = 'green'
+ elif U > T and U > TB:
+ color.append('red')
+ z90.append(24) # triangle (point up)
+ if iaverage > 90.0:
+ bgcolor.append('red')
+ else:
+ bgcolor.append('white')
+ winner = 'red'
+ else:
+ assert(False), 'Should not have come here'
+
+# else:
+# # This version does not accurately portray colors (e.g. logc_topo_brlen can be highest
+# # and yet heterophyly = 1)
+# heterotachy = float(rep['logc_topo']) > float(rep['logc_topo_brlen']) and True or False
+# heterophyly = float(rep['logc']) > float(rep['logc_topo']) and True or False
+# if heterophyly:
+# color.append('red')
+# elif heterotachy:
+# color.append('green')
+# else:
+# color.append('blue')
+
+ ndeep = float(rep['deep'])
+ deep.append(ndeep)
+ if ndeep > maxdeep:
+ maxdeep = ndeep;
+
+ dissonance = float(rep['D'])
+ d.append(dissonance)
+ if dissonance > maxdissonance:
+ maxdissonance = dissonance;
+
+ imerged = iaverage - dissonance
+
+ solid = False
+ if iaverage > 90.0:
+ solid = True
+ # z90.append(19)
+ #else:
+ # z90.append(21)
+
+ info = ''
+ info += '| Replicate: | %g |
' % replicate
+ info += '| No. conflicting: | %g |
' % nconflicting
+ info += '| No. constant: | %g |
' % nconstant
+ info += '| No. variable: | %g |
' % nvariable
+ info += '| No. pars. inform.: | %g |
' % nparsimony
+ info += '| No. deep: | %g |
' % ndeep
+ info += '| Dissonance: | %g |
' % dissonance
+ info += '| Iaverage: | %g |
' % iaverage
+ info += '| Imerged: | %g |
' % imerged
+ info += '| Theta: | %g |
' % theta
+ info += '| bigT: | %g |
' % bigT
+ info += '| Theta/bigT: | %g |
' % theta_over_bigT
+ info += '| Species tree height: | %g |
' % sheight
+ info += '| Shortest gene tree: | %g |
' % gshortest
+ info += '| Tallest gene tree: | %g |
' % gtallest
+ info += '
'
+
+ d3dataset.append('{rep: "%d", iaverage: %g, deep: %g, dissonance: %g, nconflicting: %d, nparsimony: %d, bigT: %g, thetaT: %g, solid: %s, winner: "%s", info: "%s"}' % (replicate, iaverage, ndeep, dissonance, nconflicting, nparsimony, bigT, theta_over_bigT, solid and "true" or "false", winner, info))
+
+ # Create R version
+ outf = open('%s.R' % which,'w')
+ outf.write('library(rgb)\n')
+ outf.write('rep = c(%s)\n' % ','.join(['"%d"' % x for x in labels]))
+ outf.write('x = c(%s)\n' % ','.join(['%.0f' % x for x in deep]))
+ outf.write('y = c(%s)\n' % ','.join(['%g' % y for y in d]))
+ #outf.write('ypct = c(%s)\n' % ','.join(['%g' % y for y in dpct]))
+ outf.write('z90 = c(%s)\n' % ','.join(['%d' % z for z in z90]))
+ outf.write('c = c(%s)\n' % ','.join(['"%s"' % c for c in color]))
+ outf.write('bgc = c(%s)\n' % ','.join(['"%s"' % c for c in bgcolor]))
+ outf.write('pdf("%s-Xdeep-Yd.pdf")\n' % which)
+ outf.write('plot(x,y,type="p",pch=z90,col=c,bg=bgc,main="",xlab="Number of deep coalescences",ylab="D")\n')
+ if annotate_plot:
+ outf.write('text(x, y, labels=rep, cex=0.7, pos=3)\n') # pos=3 means put label above point
+ outf.write('dev.off()\n')
+ outf.close()
+
+ # Create Javascript version
+ template_filename = 'plot-template.html'
+ stuff = open(template_filename,'r').read()
+
+ dataset = ' var dataset = [' + ','.join(d3dataset) + '];\n'
+ file_contents = re.sub('__DATASET__', dataset, stuff)
+ outf = open('%s.html' % which,'w')
+ outf.write(file_contents)
+ outf.close()
+
+if __name__ == '__main__':
+ assert len(sys.argv) == 2, 'Usage: python plot_bayes_factors.py '
+ fpath = sys.argv[1]
+ a, b = readBFSummary(fpath)
+ plotRun('a', a, False)
+ plotRun('b', b, False)
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/qsub_template.sh b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/qsub_template.sh
new file mode 100644
index 0000000..aacfd56
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/qsub_template.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+
+#$ -S /bin/bash
+#$ -cwd
+#$ -N __JOBNAME__
+#$ -q highpri.q,highmem.q
+__JOBCOMMAND__
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/summarize.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/summarize.py
new file mode 100644
index 0000000..59ba25e
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/summarize.py
@@ -0,0 +1,397 @@
+# Usage: To analyze all 100 pairs of trees.t files in lr-0-0a and lr-0-0b
+# python pairs.py lr-0-0
+import sys, os, re, glob
+from math import fabs
+import subprocess as sub
+
+prefix = None
+
+def saveGalaxListFile(fn, filenames):
+ f = open(fn, 'w')
+ for fn in filenames:
+ f.write('%s\n' % fn)
+ f.close()
+
+def runGalax(listfile, wd):
+ out, err = sub.Popen(['rungalax','--listfile',listfile,'--skip','1'], stdout = sub.PIPE, stderr = sub.PIPE, cwd=wd).communicate()
+
+ stuff = open(os.path.join(wd,'output-galax.txt'), 'r').read()
+ m = re.search('treefile(.+?)merged', stuff, re.M | re.S)
+ assert m
+ lines = m.group(1).strip().split('\n')
+ assert len(lines) - 1 in [ngenes,nruns]
+ Cdict = {}
+ Idict = {}
+ for line in lines[1:]:
+ mm = re.match('\s*(?P\S+)\s+(?P\d+)\s+(?P[0-9.]+)\s+(?P[0-9.]+)\s+(?P[0-9.]+)\s+[-][-][-]', line)
+ assert mm
+ fn = mm.group('fn')
+ gene = int(re.search('gene(\d+)', fn).group(1)) - 1
+ Cdict[gene] = float(mm.group('coverage'))
+ Idict[gene] = float(mm.group('Ipct'))
+ m = re.search('merged\s+(?P\d+)\s+(?P[0-9.]+)\s+(?P[0-9.]+)\s+(?P[0-9.]+)\s+(?P[-0-9.]+)\s*$', stuff, re.M | re.S)
+ if m is None:
+ print 'Could not find merged line in file',os.path.join(wd,'output-galax.txt')
+ print '----- stuff begins -----'
+ print stuff
+ print '----- stuff ends -----'
+ sys.exit()
+ Cmerged = float(m.group('coverage'))
+ Imerged = float(m.group('Ipct'))
+ D = float(m.group('D'))
+ return (Cdict, Cmerged, Idict, Imerged, D)
+
+def getSS(filenames):
+ ss_values = []
+ for fn in filenames:
+ if os.path.exists(fn):
+ m = re.search('([-.0-9]+) Generalized Stepping Stone method', open(fn, 'r').read(), re.M | re.S)
+ assert m
+ ss = float(m.group(1))
+ ss_values.append(ss)
+ else:
+ print 'oops, could not find file',fn
+ return ss_values
+
+def explore():
+ next_letter = 97 # ASCII position of 'a'
+ dirnames = []
+ while True:
+ suffix = chr(next_letter)
+ dirname = '%s%s' % (prefix, suffix)
+ if os.path.exists(dirname):
+ dirnames.append(dirname)
+ else:
+ break
+ next_letter += 1
+ nruns = len(dirnames)
+ assert nruns > 0
+
+ ngenes = None
+ ntaxa = None
+ nchar = None
+ for d in dirnames:
+ nexus_files = glob.glob(os.path.join(d,'gene*','*.nex'))
+ if ngenes is None:
+ ngenes = len(nexus_files)
+ else:
+ assert ngenes == len(nexus_files)
+
+ first = nexus_files[0]
+ m = re.search('dimensions\s+ntax\s*=\s*(\d+)\s+nchar\s*=\s*(\d+)\s*;', open(first,'r').read(), re.M | re.S)
+ assert m
+
+ tax = int(m.group(1))
+ if ntaxa is None:
+ ntaxa = tax
+ else:
+ assert ntaxa == tax
+
+ char = int(m.group(2))
+ if nchar is None:
+ nchar = char
+ else:
+ assert nchar == char
+ return (ngenes, ntaxa, nchar, nruns, dirnames)
+
+def mean(x):
+ assert len(x) > 0
+ sumx = sum(x)
+ lenx = len(x)
+ return sumx/lenx
+
+def diff(x):
+ if len(x) == 2:
+ return fabs(x[0] - x[1])
+ else:
+ return None
+
+def report(fn):
+ # [ run1a, run1b]
+ # Cg[0] = [1.0, 1.0]
+ # Cg[1] = [1.0, 1.0]
+ # Cg[2] = [1.0, 1.0]
+ # Cg[3] = [0.99998, 0.99999]
+ # Cg[4] = [1.0, 1.0]
+ # Cg[5] = [1.0, 1.0]
+ # Cg[6] = [1.0, 1.0]
+ # Cg[7] = [1.0, 1.0]
+ # Cg[8] = [1.0, 1.0]
+ # Cg[9] = [1.0, 1.0]
+ # Cg[10] = [0.99985, 0.99989]
+ # Ig[0] = [76.40616, 76.39408]
+ # Ig[1] = [76.39749, 76.41411]
+ # Ig[2] = [76.39469, 76.39551]
+ # Ig[3] = [98.51763, 99.00237]
+ # Ig[4] = [76.39561, 76.40318]
+ # Ig[5] = [76.39582, 76.3944]
+ # Ig[6] = [97.53833, 96.71433]
+ # Ig[7] = [89.93326, 89.74398]
+ # Ig[8] = [76.41059, 76.40221]
+ # Ig[9] = [76.3944, 76.39439]
+ # Ig[10] = [76.85768, 76.89162]
+ # Ig[11] = [82.078398, 82.025856]
+ # D = [5.22072, 5.13424]
+ # SSg[0] = [-3272.7441506 , -3272.76212639]
+ # SSg[1] = [-3235.6635948 , -3235.65019088]
+ # SSg[2] = [-3261.77896746, -3243.93983035]
+ # SSg[3] = [-3151.6675261 , -3151.66699887]
+ # SSg[4] = [-3168.72413347, -3168.71688798]
+ # SSg[5] = [-3234.25278681, -3234.25504509]
+ # SSg[6] = [-3176.83014426, -3169.05640143]
+ # SSg[7] = [-3215.85985006, -3215.86431803]
+ # SSg[8] = [-3172.57237504, -3168.62427981]
+ # SSg[9] = [-3160.7937692 , -3160.79507682]
+ # concat_ss = [-31627.11821559, -31627.09350969]
+ # logBF = [-28422.02948581, -28424.960394125]
+
+ if False:
+ # for debugging: produce summary like that show above
+ for i in range(ngenes+1):
+ print 'Cg[%d] =' % i,Cg[i]
+ for i in range(ngenes+2):
+ print 'Ig[%d] =' % i,Ig[i]
+ print 'D =',D
+ for i in range(ngenes):
+ print 'SSg[%d] =' % i,SSg[i]
+ print 'concat_ss =',concat_ss
+ print 'logBF =',logBF
+ raw_input('..')
+
+ outf = open(fn,'w')
+
+ meanDmcmc = []
+ meanSS = []
+ meanLogBF = []
+ outf.write('%12s %12s %12s %12s %12s %12s %12s %12s %12s\n' % ('gene','run','C','I','SS','Dmcmc','Cdiff','Idiff','SSdiff'))
+ for g in range(ngenes):
+ outf.write('\n')
+ for r in range(nruns):
+ if len(Cg[g]) > r:
+ C = '%12.5f' % Cg[g][r]
+ else:
+ C = '%12s' % '---'
+ if len(Ig[g]) > r:
+ I = '%12.5f' % Ig[g][r]
+ else:
+ I = '%12s' % '---'
+ if len(SSg[g]) > r:
+ SS = '%12.5f' % SSg[g][r]
+ else:
+ SS = '%12s' % '---'
+ outf.write('%12d %12d %s %s %s %12s %12s %12s %12s\n' % (g+1,r+1,C,I,SS,' ',' ',' ',' '))
+ if diff(Cg[g]) is None:
+ diffC = '---'
+ else:
+ diffC = '%12.5f' % diff(Cg[g])
+ if diff(Ig[g]) is None:
+ diffI = '---'
+ else:
+ diffI = '%12.5f' % diff(Ig[g])
+ if diff(SSg[g]) is None:
+ diffSS = '---'
+ else:
+ diffSS = '%12.5f' % diff(SSg[g])
+ outf.write('%12s %12s %12.5f %12.5f %12.5f %12.5f %12s %12s %12s\n' % (' ',' ',mean(Cg[g]),mean(Ig[g]),mean(SSg[g]),Dmcmc[g],diffC, diffI, diffSS))
+ meanDmcmc.append(Dmcmc[g])
+ meanSS.append(mean(SSg[g]))
+ outf.write('\n')
+
+ lnLtop = sum(concat_ss)/len(concat_ss)
+ lnLbottom = sum(meanSS)
+ meanLogBF = lnLtop - lnLbottom
+
+ headers = ''
+ headers += '%12s' % 'jobid'
+ headers += '%12s' % 'T'
+ headers += '%12s' % 'theta/T'
+ headers += '%12s' % 'Iavg'
+ headers += '%12s' % 'D'
+ headers += '%12s' % 'deep'
+ headers += '%12s' % 'logBF'
+ headers += '%12s' % 'maxDmcmc'
+ headers += '%12s' % 'Imerged'
+ headers += '%12s' % 'Cmerged'
+ headers += '%12s' % 'maxSS'
+ headers += '%12s' % 'STH'
+ headers += '%12s' % 'minGTH'
+ headers += '%12s' % 'maxGTH'
+ headers += '%12s' % 'mseed'
+ headers += '%12s' % 'dseed'
+ headers += '%12s' % 'aseed'
+ headers += '\n'
+ outf.write(headers)
+
+ for r in range(nruns):
+ values = ''
+ values += '%12s' % jobid[r] # jobid
+ values += '%12.5f' % T[r] # T
+ values += '%12.5f' % (theta[r]/T[r]) # theta/T
+ values += '%12.5f' % Ig[ngenes+1][r] # Iavg
+ values += '%12.5f' % D[r] # D
+ values += '%12.5f' % deep[r] # deep
+ values += '%12.5f' % logBF[r] # logBF
+ values += '%12s' % '---' # maxDmcmc
+ values += '%12.5f' % Ig[ngenes][r] # Imerged
+ values += '%12.5f' % Cg[ngenes][r] # Cmerged
+ values += '%12s' % '---' # maxSS
+ values += '%12.5f' % sth[r] # STH
+ values += '%12.5f' % mingth[r] # minGTH
+ values += '%12.5f' % maxgth[r] # maxGTH
+ values += '%12d' % mseed[r] # mseed
+ values += '%12d' % dseed[r] # dseed
+ values += '%12d' % aseed[r] # aseed
+ values += '\n'
+ outf.write(values)
+
+ outf.write('------------ '*17 + '\n')
+
+ values = ''
+ values += '%12s' % prefix # jobid
+ values += '%12.5f' % mean(T) # T
+ values += '%12.5f' % (mean(theta)/mean(T)) # theta/T
+ values += '%12.5f' % mean(Ig[ngenes+1]) # Iavg
+ values += '%12.5f' % mean(D) # D
+ values += '%12.5f' % mean(deep) # deep
+ values += '%12.5f' % meanLogBF # logBF
+ values += '%12.5f' % max(meanDmcmc) # maxDmcmc
+ values += '%12.5f' % mean(Ig[ngenes]) # Imerged
+ values += '%12.5f' % mean(Cg[ngenes]) # Cmerged
+ values += '%12.5f' % max(meanSS) # maxSS
+ values += '%12.5f' % mean(sth) # STH
+ values += '%12.5f' % mean(mingth) # minGTH
+ values += '%12.5f' % mean(maxgth) # maxGTH
+ values += '%12d' % mean(mseed) # mseed
+ values += '%12d' % mean(dseed) # dseed
+ values += '%12s' % '---' # aseed
+ values += '\n'
+ outf.write(values)
+
+ outf.close()
+
+if __name__ == '__main__':
+ assert len(sys.argv) == 2
+ prefix = sys.argv[1]
+ ngenes, ntaxa, nchar, nruns, dirnames = explore()
+ print 'ngenes =',ngenes
+ print 'ntaxa =',ntaxa
+ print 'nchar =',nchar
+ print 'nruns =',nruns
+ letter = [chr(i + 97) for i in range(nruns)]
+
+ jobid = [] # names used for each run
+ mseed = [] # master seed used to generate this run
+ dseed = [] # seeds used to generate data (one for each run)
+ aseed = [] # seeds used to for replicate MCMC analyses (one for each run)
+ theta = [] # theta used for simulating data (one for each run, but all should be identical)
+ T = [] # T used for simulating data (one for each run, but all should be identical)
+ deep = [] # true number of deep coalsecences (one for each run)
+ sth = [] # true species tree height (one for each run)
+ mingth = [] # true minimum gene tree height (one for each run)
+ maxgth = [] # true maximum gene tree height (one for each run)
+ D = [] # D (one for each run)
+ Cg = {g:[] for g in range(ngenes+1)} # coverage for each gene plus merged
+ Ig = {g:[] for g in range(ngenes+2)} # Ipct for each gene plus merged and average
+ SSg = {g:[] for g in range(ngenes)} # p(D) for each gene
+ Dmcmc = {g:[] for g in range(ngenes)} # D value for replicate MCMC analyses for each gene
+ logBF = []
+
+ # if prefix='run1', dirnames = ['run1a','run1b']
+ for dirname in dirnames:
+ print 'processing directory %s...' % dirname
+
+ # gist-output.txt file provides information about true species tree and gene trees
+ stuff = open(os.path.join(dirname, 'gist-output.txt'), 'r').read()
+ jobid.append(re.search('outdir\s+=\s+(\S+?)$', stuff, re.M | re.S).group(1))
+ mseed.append(int(re.search('master_seed\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+ dseed.append(int(re.search('data_seed\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+ aseed.append(int(re.search('analysis_seed\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+ theta.append(float(re.search('theta\s+=\s+([-.0-9e]+?)$', stuff, re.M | re.S).group(1)))
+ T.append(float(re.search('T\s+=\s+([-.0-9e]+?)$', stuff, re.M | re.S).group(1)))
+ deep.append(float(re.search('Total deep coalescences\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+ sth.append(float(re.search('Species tree height\s+=\s+([-.0-9e]+?)$', stuff, re.M | re.S).group(1)))
+ mingth.append(float(re.search('Shortest gene tree\s+=\s+([-.0-9e]+?)$', stuff, re.M | re.S).group(1)))
+ maxgth.append(float(re.search('Tallest gene tree\s+=\s+([-.0-9e]+?)$', stuff, re.M | re.S).group(1)))
+ assert ngenes == int(int(re.search('number_of_genes\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+ assert ntaxa == int(int(re.search('number_of_species\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+ assert nchar == int(int(re.search('gene_length\s+=\s+(\d+?)$', stuff, re.M | re.S).group(1)))
+
+ # run galax to estimate information content
+ Cdict, Cmerged, Idict, Imerged, d = runGalax('galax.txt',dirname)
+ Iavg = sum([Idict[k] for k in Idict.keys()])/ngenes
+
+ # output-galax.txt from run1a
+ # treefile unique coverage I Ipct D
+ # gene1/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene2/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene3/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene4/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene5/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene6/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene7/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene10/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene9/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene8/trees.t 2 1.00000 4.65206 99.95910 ---
+ # merged 2 1.00000 4.65372 99.99492 0.00099
+ #
+ # output-galax.txt from run1b
+ # treefile unique coverage I Ipct D
+ # gene1/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene2/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene3/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene4/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene5/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene6/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene7/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene8/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene9/trees.t 1 1.00000 4.65396 100.00000 ---
+ # gene10/trees.t 1 1.00000 4.65396 100.00000 ---
+ # merged 1 1.00000 4.65396 100.00000 -0.00000
+ for g in range(ngenes): # [run1a, run1b]
+ Cg[g].append(Cdict[g]) # Cg[7] = [1.0, 1.0 ]
+ Ig[g].append(Idict[g]) # Ig[7] = [99.95910, 100.00000]
+ Cg[ngenes].append(Cmerged) # Cg[10] = [1.0, 1.0 ]
+ Ig[ngenes].append(Imerged) # Ig[10] = [99.99492, 100.00000]
+ Ig[ngenes+1].append(Iavg) # Ig[11] = [99.99591, 100.00000]
+ D.append(d) # D = [0.00099, 0.00000 ]
+ logBF.append(0.0)
+
+ # Cg, Ig, SSg, Dmcmc
+ for g in range(ngenes):
+ gg = g + 1
+
+ print 'Processing gene %d...' % gg
+ treefilenames = []
+ ssfilenames = []
+ for dirname in dirnames:
+ treefn = os.path.join(dirname, 'gene%d' % gg, 'trees.t')
+ ssfn = os.path.join(dirname, 'gene%d' % gg, 'ss-sump-log.txt')
+ treefilenames.append(treefn)
+ ssfilenames.append(ssfn)
+ if nruns > 1:
+ listfname = 'galax-mcmc%d.txt' % g
+ saveGalaxListFile(listfname, treefilenames)
+ Cdict, Cmerged, Idict, Imerged, d = runGalax('galax-mcmc%d.txt' % g,'.')
+ Dmcmc[g]= d
+ os.remove(listfname)
+
+ ss = getSS(ssfilenames) # ssfilenames = ['run1a/gene1/ss-sump-log.txt', 'run1b/gene1/ss-sump-log.txt']
+ SSg[g] = ss # SSg[0] = [-4237.74992153, -4237.73491098 ]
+
+ # ['run1a/concatenated/ss-sump-log.txt', 'run1b/concatenated/ss-sump-log.txt' ]
+ # concat_ss = [-41947.11565374, -41947.11893950 ]
+ concat_ss = getSS([os.path.join(dirname, 'concatenated', 'ss-sump-log.txt') for dirname in dirnames])
+
+ logBF = []
+ for i,dirname in enumerate(dirnames):
+ lnLtop = concat_ss[i]
+ lnLbottom = sum([SSg[g][i] for g in range(ngenes)])
+ logBF.append(lnLtop - lnLbottom)
+
+ # cleanup
+ os.remove('output-galax-details.txt')
+ os.remove('output-galax-merged.tre')
+ os.remove('output-galax.txt')
+
+ report('summary-%s.txt' % prefix)
+
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/tree.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/tree.py
new file mode 100644
index 0000000..f210c20
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/tree.py
@@ -0,0 +1,140 @@
+import random
+
+class node(object):
+ def __init__(self, ndnum): # initialization function
+ self.rsib = None # right sibling
+ self.lchild = None # left child
+ self.par = None # parent node
+ self.number = ndnum # node number
+ self.edgelen = 0.0 # branch length
+ self.height = 0.0 # height above root
+ self.deep = False # True if this node resulted from a deep coalescence
+ self.descendants = set([ndnum]) # set containing descendant leaf set
+
+def joinRandomPair(node_list, next_node_number, is_deep_coalescence):
+ # pick first of two lineages to join and delete from node_list
+ i = random.randint(1, len(node_list))
+ ndi = node_list[i-1]
+ del node_list[i-1]
+
+ # pick second of two lineages to join and delete from node_list
+ j = random.randint(1, len(node_list))
+ ndj = node_list[j-1]
+ del node_list[j-1]
+
+ # join selected nodes and add ancestor to node_list
+ ancnd = node(next_node_number)
+ ancnd.deep = is_deep_coalescence
+ ancnd.lchild = ndi
+ ancnd.descendants = set()
+ ancnd.descendants |= ndi.descendants
+ ancnd.descendants |= ndj.descendants
+ ndi.rsib = ndj
+ ndi.par = ancnd
+ ndj.par = ancnd
+ node_list.append(ancnd)
+
+ return node_list
+
+def joinSpecificPair(node_list, i, j, next_node_number, is_deep_coalescence):
+ assert j != i
+
+ ndi = None
+ for k,nd in enumerate(node_list):
+ if nd.number == i:
+ ndi = node_list.pop(k)
+ assert ndi is not None
+
+ ndj = None
+ for k,nd in enumerate(node_list):
+ if nd.number == j:
+ ndj = node_list.pop(k)
+ assert ndj is not None
+
+ # join selected nodes and add ancestor to node_list
+ ancnd = node(next_node_number)
+ ancnd.deep = is_deep_coalescence
+ ancnd.lchild = ndi
+ ancnd.descendants = set()
+ ancnd.descendants |= ndi.descendants
+ ancnd.descendants |= ndj.descendants
+ ndi.rsib = ndj
+ ndi.par = ancnd
+ ndj.par = ancnd
+ node_list.append(ancnd)
+
+ return node_list
+
+def getPreorder(nd, start = True):
+ global _preorder
+ if start:
+ _preorder = []
+
+ _preorder.append(nd)
+
+ if nd.lchild:
+ getPreorder(nd.lchild, False)
+
+ if nd.rsib:
+ getPreorder(nd.rsib, False)
+
+ return _preorder
+
+def flipAllNodesAbove(root):
+ node_list = getPreorder(root)
+ for nd in node_list:
+ if nd.lchild:
+ lc = nd.lchild
+ rc = lc.rsib
+ nd.lchild = rc
+ assert rc.rsib is None
+ rc.rsib = lc
+ lc.rsib = None
+
+def makeNewick(nd, brlen_scaler = 1.0, start = True):
+ global _newick
+ global _TL
+ if start:
+ _newick = ''
+ _TL = 0.0
+
+ if nd.lchild:
+ _newick += '('
+ makeNewick(nd.lchild, brlen_scaler, False)
+ else:
+ blen = nd.edgelen*brlen_scaler
+ _TL += blen
+ _newick += '%d:%.5f' % (nd.number, blen)
+
+ if nd.rsib:
+ _newick += ','
+ makeNewick(nd.rsib, brlen_scaler, False)
+ elif nd.par is not None:
+ blen = nd.par.edgelen*brlen_scaler
+ _TL += blen
+ _newick += '):%.5f' % blen
+
+ return _newick, _TL
+
+def calcActualHeight(root):
+ h = 0.0
+ nd = root
+ while nd.lchild:
+ nd = nd.lchild
+ h += nd.edgelen
+ return h
+
+def sumEdgeLengths(root):
+ sum_edge_lengths = 0.0
+ pre = getPreorder(root)
+ for nd in pre:
+ sum_edge_lengths += nd.edgelen
+ return sum_edge_lengths
+
+def countDeepCoalescences(root):
+ n = 0
+ preorder = getPreorder(root)
+ for nd in preorder:
+ if nd.deep:
+ n += 1
+ return n
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/tree.pyc b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/tree.pyc
new file mode 100644
index 0000000..d9a07c1
Binary files /dev/null and b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/tree.pyc differ
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/yule.py b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/yule.py
new file mode 100644
index 0000000..434af30
--- /dev/null
+++ b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/yule.py
@@ -0,0 +1,68 @@
+import random
+import tree
+
+# calcPhi computes sum_{K=2}^S 1/K, where S is the number of leaves in the tree
+# - num_species is the number of leaves (tips) in the tree
+def calcPhi(num_species):
+ phi = sum([1.0/(K+2.0) for K in range(num_species-1)])
+ return phi
+
+# yuleTree creates a species tree in which edge lengths are measured in
+# expected number of substitutions.
+# - num_species is the number of leaves
+# - mu_over_s is the mutations-per-generation/speciations-per-generation rate ratio
+def yuleTree(num_species, mu_over_s):
+ # create num_species nodes numbered 1, 2, ..., num_species
+ nodes = [tree.node(i+1) for i in range(num_species)]
+
+ next_node_number = num_species + 1
+ while len(nodes) > 1:
+ # choose a speciation time in generations
+ K = float(len(nodes))
+ mean_epoch_length = mu_over_s/K
+ t = random.gammavariate(1.0, mean_epoch_length)
+
+ # update each node's edgelen
+ for n in nodes:
+ n.edgelen += t # same as: n.edgelen = n.edgelen + t
+
+ nodes = tree.joinRandomPair(nodes, next_node_number, False)
+ next_node_number += 1
+
+ return nodes[0]
+
+# calcExpectedHeight returns the expected height of the species tree in terms of
+# expected number of substitutions from the root to one tip.
+# - num_species is the number of leaves
+# - mu_over_s is the mutations-per-generation/speciations-per-generation rate ratio
+def calcExpectedHeight(num_species, mu_over_s):
+ return mu_over_s*calcPhi(num_species)
+
+if __name__ == "__main__":
+ random_seed = 8699499 # 7632557, 12345
+ number_of_species = 367
+ mutation_speciation_rate_ratio = 15.0 # 0.689655172 # yields tree height 1 for 6 species
+ random.seed(random_seed)
+ species_tree_root = yuleTree(number_of_species, mutation_speciation_rate_ratio)
+ newick = tree.makeNewick(species_tree_root)
+ print 'Random number seed: %d' % random_seed
+ print 'Simulating one tree:'
+ print ' number of species = %d' % number_of_species
+ print ' mutation-speciation rate ratio = %g' % mutation_speciation_rate_ratio
+ print ' actual tree length =',newick[1]
+ expected_height = calcExpectedHeight(number_of_species, mutation_speciation_rate_ratio)
+ print ' expected height =',expected_height
+ actual_height = tree.calcActualHeight(species_tree_root)
+ print ' actual height =',actual_height
+ print ' newick: ',newick[0]
+
+ if False:
+ nreps = 10000
+ print 'Simulating %d trees...' % nreps
+ h = []
+ for i in range(nreps):
+ species_tree_root = yuleTree(number_of_species, mutation_speciation_rate_ratio)
+ actual_height = tree.calcActualHeight(species_tree_root)
+ h.append(actual_height)
+ print ' expected height =',expected_height
+ print ' average actual height =',sum(h)/len(h)
diff --git a/Sys_Bio_Suppl_Feb25_2019/simulations/gist/yule.pyc b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/yule.pyc
new file mode 100644
index 0000000..7da628e
Binary files /dev/null and b/Sys_Bio_Suppl_Feb25_2019/simulations/gist/yule.pyc differ